TRoVe: Discovering Error-Inducing Static Feature Biases in Temporal Vision-Language Models
Authors: Maya Varma
Jean-Benoit Delbrouck
Sophie Ostmeier
Akshay S Chaudhari
Curtis Langlotz
Venue: NeurIPS 2025 poster ()
Keywords: temporal
biases
vision-language
error discovery
medical
video
Primary area: evaluation
TL;DR: We introduce TRoVe, an automated approach for discovering error-inducing static feature biases learned by temporal VLMs.

Abstract: Vision-language models (VLMs) have made great strides in addressing temporal understanding tasks, which involve characterizing visual changes across a sequence of images. However, recent works have suggested that when making predictions, VLMs may rely on static feature biases, such as background or object features, rather than dynamic visual changes. Static feature biases are a type of shortcut and can contribute to systematic prediction errors on downstream tasks; as a result, identifying and characterizing error-inducing static feature biases is critical prior to real-world model deployment. Existing approaches for identifying such systematic failure modes in trained models (i) are typically designed for non-temporal settings and (ii) are challenging to evaluate in temporal settings due to the lack of quantitative evaluation frameworks. In this work, we address these challenges by introducing TRoVe, an automated approach for discovering error-inducing static feature biases learned by temporal VLMs. Given a trained VLM and an annotated validation dataset associated with a downstream classification task, TRoVe extracts candidate static features from the dataset and scores each feature by (i) the effect of the feature on classification errors as well as (ii) the extent to which the VLM relies on the feature when making predictions. In order to quantitatively evaluate TRoVe, we introduce an evaluation framework consisting of 101 trained temporal VLMs paired with ground-truth annotations for learned static feature biases. We use this framework to demonstrate that TRoVe can accurately identify error-inducing static feature biases in VLMs, achieving a 28.6% improvement over the closest baseline. Finally, we apply TRoVe to 7 off-the-shelf VLMs and 2 temporal understanding tasks, surfacing previously-unknown static feature biases and demonstrating that knowledge of learned biases can aid in improving model performance at test time. Our code is available at https://github.com/Stanford-AIMI/TRoVe.

TRoVE:
Discovering Error-Inducing Static Feature Biases in Temporal Vision-Language Models
Maya Varmal Jean-Benoit Delbrouck Sophie Ostmeier'
Akshay Chaudhari
Curtis Langlotz
Stanford University "HOPPR
mayavarma&cs. stanford
Abstract
Vision-language models (VLMs) have made great strides in addressing temporal understanding tasks, which involve characterizing visual changes across a sequence of images. However, recent works have suggested that when making predictions, VLMs may rely on static feature biases, such as background or object features, rather than dynamic visual changes. Static feature biases and can contribute to systematic prediction errors on downstream tasks; as a result, identifying and characterizing error-inducing static feature biases is critical prior to real-world model deployment. Existing approaches for identifying such systematic failure modes in trained models (i) are typically designed for non- temporal settings and (ii) are challenging to evaluate in temporal settings due to this work, address these
type of shorteut
the lack of quantitative evaluation frameworks. challenges by introducing TROVE, an automated approach for discovering error- inducing static feature biases learned by temporal VLMs. Given a trained VLM and an annotated validation dataset associated with a downstream classification task, TROVE extracts candidate static features from the dataset and scores each feature by (i) the effect of the feature on classification errors as well as (ii) the extent which the VLM relies on the feature when making predictions. In order to quantitatively evaluate TROVE, we introduce an evaluation framework consisting of 101 trained temporal VLMs paired with ground-trute annotations for learned static feature biases. We use this framework to demonstrate that TROVE can accurately identify error-inducing static feature biases VLMs, achieving a 28.6% improvement over the closest baseline. Finally, we apply TROVE 7 off-the-shelf VLMs and temporal understanding tasks, surfacing previously- unknown static feature biases and demonstrating that knowledge of learned biases can aid in improving model performance at test time. Our code available https: // tiu......... cot com/Stanford-AIM/ T
Introduction
Vision-language models (VLMs) capable of jointly processing visual and textual data have been shown to possess state-of-the-a art reasoning abilities (147]. In particular, given input sequence with multiple images collected across varying timepoints, temporal VLMs can effectively characterize visual changes over time, a capability known temporal understanding [8-15] For example, tempo- ral VLMS can recognize human actions given a sequence of video frames 11 and characterize disease progression given longitudinal medical images [8
Models designed to perform temporal understanding tasks often demonstrate high overall perfor- mance; however, recent works have demonstrated that such models may be affected by static feature biases, a phenomenon where models utilize static patterns (e.g. image background or a particular
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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object in the scene) as shortcuts when making predictions rather than analyzing dynamic visual changes occurring across the image sequence 20], As an illustrative example, consider videos from an activity recognition dataset with the class label "cl "climbing tree", which depict people climbing up or down trees (Figure []. temporal VI.M is tasked with accepting a sequence of video frames as input and then classifying the action being performed by the person in the scene. In this scenario, a VLM that relies on static feature biases may base predictions for the class label "climbing tree" solely the presence of trees and foliage, rather than analyzing the true motion patterns associated with a person climbing a tree, At inference time, the performance of this VLM will depend heavily on whether the static feature is present; consequently, as illustrated in Figure I the VLM is likely to make systematic prediction errors when classifying videos from other classes something") with prominently visible trees.
(e.g. "swinging
Identifying learned static feature biases that con- tribute to systematic prediction errors is critical prior to real-world model deployment (21). Tra- ditional approaches for detecting such failure modes, which typically involve a combination of manual analysis and pixel-wise interpretabil- ity algorithms (e.g. GradCAM), require exten- sive human effort and are cone-consuning to implement at scale, particularly as the length of the input sequence increases [22] (3). This
Class Label: climbing tree
Temporal VLM
climbing tree
Class Label: swinging on something
swinging on something
VLM
suggests the need for automated approaches; however, performing automated identification of error-inducing static feature biases is chal- lenging for the following two reasons. First, existing automated approaches for discovering systematic errors are designed for non-temporal (e.g. single image) settings (24. Such ap- proaches, which typically operate by analyzing model predictions on a labeled validation dataset and surfacing coherent groups of misclassified samples, are not adequate for discovering static feature biases in settings where each data sam- ple consists of sequence of multiple, temporally linked images. Second, performing quantitative evaluations of automated approaches in the temporal setting is complicated by the fact that the ground-truth static feature biases of pretrained models are typically unknown; as a result, it is difficult to ascertain whether biases extracted by automated methods are indeed accurate
Tamporal
climbing tree
Figure I: Temporal models often rely on the pres- ence of static feature biases, such as background features objects in the scene, when making predictions. In this example, the VideoCLIP-X model [12] makes systematic prediction errors on the class swinging on something when trees are present.
In this work, we address these challenges by introducing TROVE, an automated approach for improving Temporal Robustness of Vision-Language models. Given a pretrained VLM, our goal is to discover learned static feature biases that contribute to systematic prediction errors on downstream temporal understanding tasks. Knowledge of such static feature biases (e.g. trees in the previously- discussed example) can enable developer to better understand and address model failure modes prior to weal-world deployment. To this end, TROVE operates on a labeled validation dataset by first decomposing each input multi-image sequence into constituent images and grouping visually- similar images into clusters. Here, each cluster represents a particular feature occurring consistently throughout the dataset. We then introduce scoring function that ranks each feature by (i) the effect of the feature on classification errors as well as (ii) the extent to which the VLM relies on the feature when making predictions. As output, TRoVE generates list of identified static feature biases paired with affected class labels.
In order to assess the utility of our approach, we design an evaluation framework consisting of 101 temporal VLMs trained on synthetic data. We pair each VLM with annotations for ground-truth error- inducing static feature biases, enabling rigorous quantitative analyses. Across this suite of models, TROVE accurately discovers error-inducing static feature biases, achieving a 28.6 88.6% improvement over the closest baseline. We find that TROVE operates effectively across range of static feature bias types (background biases, object biases, and attribute biases) and input sequence lengths.
Given the strong performance of TROVE on synthetic experimental settings, we then extend TROVE to real-world temporal VLMs. Across suite of seven ofte-offthe-art VLMs and two temporal

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understanding tasks (activity recognition in videos and disease progression classification in med- ical images), TROVE accurately surfaces previously-unknowe static feature biases. We further demonstrate that knowledge of performance; we present an approach for mitigating prediction errors without the need for data augmentation or VLM retraining, resulting in performance improvements on an activity recognition task of up to 1 111% on sequences containing static features.
features can aid in improving test-time VI.M
Ultimately, TROVE demonstrates strong practical utility and can serve as an effective tool for evaluating and improving robustness of temporal VLMs.
2 Related Work
Discovering systematic errors in non-temporal settings: Since subgroup labels are typically unavailable in datasets, identifying and understanding critical failure modes of models can be challenging. Early approaches paired visualization techniques with humans in the loop to identify model failures [28] 22]; however, this approach is time-consuming and difficult to scale effectively across large numbers of models and tasks. To address this challenge, recent line of work has proposed automated approaches for the task identifying systematic prediction errors made by models; notable methods in this domain include Domino (24). Distilling Failures [25], and George 26), among others [19 Such methods typically analyze model predictions on a labeled validation set and identify features in the data (e.g. specific visual cue in image settings) that systematically contribute to mispredicted labels. Model developers can then use these identified error patterns to update models prior to deployment (2].
Although such approaches have been shown to be effective, these methods are predominantly designed for non-temporal settings, where each input sample (e.g. image, text) can be represented as single entity. In contrast, input samples in the temporal setting take the form of multi-image sequences, and collapsing an entire sequence to single entity provides inadequate granularity for detecting errors resulting from static feature biases. As we demonstrate Section 4 naive extensions of such methods to temporal settings fail to work effectively, particularly when error-inducing features are visible in only a subset of the sequence.
Our approach also builds upon recent fine tne-grained approaches in the image setting that leverage region-level information for detecting systematic errors (27 33]. However, these approaches are designed and evaluated solely on non-temporal, single-image settings.
Discovering systematic errors in temporal settings: Prior works have noted that temporal models often rely on static feature biases as shortcuts when making predictions (00 34) In such settings, using just single frame input a model can result high performance on multi-image temporal understanding tasks [17 (2). A range of approaches have been proposed for reducing model reliance on static feature biases, predominantly in the context of background biases in video-based activity
39]; such approaches typically involve novel optimization procedures or data augmentation strategies. We draw a key distinction between these works and our approach: whereas this line of work focuses explicitly on mitigating the influence of static feature biases during the model training procedure, our work instead aims to accurately discover learned biases given a pretrained temporal model. We also extend beyond the human activity recognition setting, including evaluations on both a synthetic task as well as a medical imaging task.
recognition tasks 37 16
18
19
38
Our Approach: TROVE
We now introduce TROVE, approach for improving temporal robustness of VLMs by discovering learned static feature biases that contribute to systematic prediction errors. In Section 3.1 we formally describe our problem setting. We then present methodological details for TROVE in Section 3.2 An overview of TROVE is provided in Figure 2
3.1 Preliminaries
VLMS designed to perform temporal understanding tasks are generally trained on large-scale datasets of the form D ((S,, T where S, represents a multi-image sequence and T; represents paired text in the form of a caption or description. Each input sequence S, can be expressed as
3

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Class Label: swinging something
Class Label: climbing tree BERRPTRER TITT FETTE
candidate static features
Step
Step
Discover error- piror-inducing--ee static feature blases learned VLMs
Does feature contribute predictioon
the feature learned
bries?
VEM
with Fecture
Sccachicss
VLM
Predicsion Contideer
without fouturr
Output:
Static Feature
Discovered
Labal
malligging oz something mrg
Bion
Figure 2: TROVE is automated approach for discovering error-inducing static feature biases learned by temporal Vs.s. In this example, TRoVE identifies a static feature bias associated with trees, which results in degraded performance the class label swinging on something (40).
8 ( ), where each I; represents single image and n, represents the total number of images in sequence S,. At inference time, temporal VLMs are evaluated using downstream tasks that assess the ability of the model to understand visual changes over time (e.g. activity recognition, disease progression classification). By definition, an effective downstream temporal understanding task will require the model to parse multi-image sequence and analyze dynamic visual patterns.
In this work, we focus explicitly on downstream temporal understanding tasks formulated as clas- sification problems, where inference datasets are expressed as Dy = {(S,, y4 }=) for sequences S; and class labels y E y. Here, y represents the ground -tuth label set associated with the task, and we assume that m, 1 for all sequences S: DD. Recent works have suggested that when making predictions at inference time, models trained to perform temporal understanding tasks may rely heavily on static feature biases, such background features or objects in the scene, rather than rely on the true dynamic visual changes [16 34]. For example, as shown in Figure [ the eecently-introduced VideoCLIP-XL model relies on the presence of trees, static feature, when assigning predictions for the class label y climbing tree [2]. Static feature biases are a type of shortcut and can ultimately result in systematic prediction errors inference time; in Figure I this manifests as low performance on other classes in y My} when the static feature is present, such as the class label swinging on something.
3.2 Discovering Static Feature Biases
Our key goal in this work is to discover static features that meet the following two criteria. First, identified static features should be error-inducing, meaning that the presence of the feature within a sequence directly contributes to prediction errors on the downstream temporal understanding task of interest. Second, identified static features should reflect a learned model bias, suggesting that the model relies on the presence of the feature when making predictions. However, designing an automated algorithm that satisfies these two criteria is challenging, since image-level static features are typically not annotated in temporal datasets and may only occur in subset of images within sequence. For instance, as shown in Figure [ image-level annotations for trees are not available, and trees only visible in portion of the sequence; this complicates the process of discovering the association between the static feature and observed prediction errors.
We now introduce TROVE, which identifies static feature biases learned by temporal VLMs. In line with the criteria described above, TROVE aims to identify static features that both contribute directly to downstream prediction errors and represent learned model biases. Given pretrained VLM F and a validation dataset Dv associated with a downstream temporal understanding task, TROVE operates by (1) extracting candidate static features that occur consistently throughout sequences in Dv and (2) scoring each feature by both its effect on prediction errors made by VLM and the extent which the feature represents a learned bias by VLM F. Importantly, proposed approach does not require

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image-level static feature annotations and can operate effectively even when static features occur in only a subset of the sequence.
Extracting candidate static features. Given the labeled validation dataset Dv. the first step in our approach is to extract candidate static features. To this end, we begin by retrieving all multi- image sequences S; in Dy. Since our goal is to identify static feature biases that manifest at the image level, we compute image-level embeddings for each image I, contained within input sequence S (4 12 :..,, ). To generate an image-level embedding for 4, we create a new static sequence consisting of the single image I replicated n, times, effectively removing all temporal variation; we then use the vision encoder of VLM F to compute an embedding for this sequence.
In order to identify static features that occur consistently within dataset Dy. cluster the computed image-level embeddings using spherical K-means with cosine distance. The optimal number of clusters is selected automatically by sweeping across range of potential values and selecting the number that maximizes the Silhouette score. At the end of this step, we obtain a collection of clusters C, where each cluster C' C represents a set of images with shared feature; for instance, in the example from Figure 2 one cluster in C. may consist of frames with prominently-visible trees.
Discovering error-inducing static feature biases. The second step in our approach is to determine the extent to which a candidate feature represented by cluster C' both (i) contributes to prediction errors and (ii) represents a static bias learned by temporal VLM F. To this end, introduce two-pronged scoring function designed to characterize each of these factors. First, for a given cluster C, the error contribution score (FCS) evaluates whether VLM F makes systematic prediction emrors on one or more classes when static features associated with C are present. Second, the static bias score (SB evaluates whether VLM F has learned bias associated with static features in C; this involves determining the extent to which model I' relies on static features in C' when making predictions. We discuss these components in detail below.
For each cluster C E C representing a candidate static feature, we first identify all multi-image sequences S, E Dy with at least one constituent image in cluster C. Let Ve represent the set of ground-trute class labels associated with these sequences. For cluster C and class label y E Ve, we compute the error contribution score ECSV follows:
FCS
acc" c acc%
Here, accy represents classification accuracy on all multi-image sequences S; Dy with at least one constituent image in cluster C' and ground-truth label y. Conversely, acck represents classification accuracy on all multi-image sequences S. E Dy with constituent images in cluster e and ground- truth label y. The error contribution score ECSV ranges between 1 and 1. Large positive values of ECS% suggest that when static features associated with C are present in sequence, the VLM F is likely to demonstrate degraded performance on class label y.
Next, for cluster C and class label y E Vc. we compute the static bias score SBS Let iJ refer to the label predicted by model F for an input sequence S, E Dy. We first filter cluster C to retain only images 1; where the corresponding sequence St (1) has ground-truth label y and (2) is mispredicted (i.c. 2; * y). We refer to this set as Cwrrnn S C. We then use VLM F to classify each image I, E Cwrong using the full label set Y; in order to do so, utilize the same procedure discussed previously, where we provide a static sequence consisting of image J, repeated n; times as input to the vision encoder associated with model F. Our insight here is that the downstream temporal classification task, by definition, requires dynamic sequence with visual changes in order to be successfully solved. As result, model that generates high-confidence predictions when provided with only a static, unchanging sequence as input is likely relying on a learned static bias. Based on this insight, we compute the static bias score:
SBSS-
softmax(F([,, 1.))),
C Currongl
I:COwromg
The static bias score ranges between 0 and 1, with large values of SBSV suggesting that model F has learned to rely on the static feature when making predictions. We also calibrate model confidences via temperature scaling prior computing the static bias score [41).
Finally, for each cluster C and label y, we compute a sum of the error contribution score and the static bias score as follows: ECS/ + SBSS. This quantity, which we refer to as the TRoVE score,
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Static Visual Feature Type: background
Static Visual Type: object
Static Visual Feature Type: attribute
Sequence:
Sequence:
Sequence:
Caption:
Caption: "moving
Caption: "moving south"
Figure 3: Example five-image sequences with injected static visual features. Our evaluation frame- work considers three types of static visual features background, object, and attribute that can contribute to biased model predictions on temporal understanding tasks.
can be used to measure the extent to which each static feature (i) contributes to prediction enrors and (ii) represents static bias learned by VLM F. An ablation on the role the two components of the TROVE score is provided in Appendix B
Evaluating TRoVE in Synthetic Settings
Evaluating TROVE is complicated by the fact that ground-truth biases learned by a VLM are typically unknown a priori; thus, it is challenging to assess whether discovered biases are indeed accurate. In order address this challenge, we introduce a large-scale quantitative evaluation framework that leverages synthetic data. Our approach is motivated by prior works [24] 27 42 yet introduces novel setup that focuses on temporal settings with multi-image sequence inputs. Section 4.1 we discuss details related to the construction of our evaluation framework. Then, in Section 4.2 we demonstrate quantitatively that TROVE can effectively surface error-inducing static feature biases learned by VLMs, achieving 28.6% improvement over the closest baseline.
We emphasize here that the of synthetic data provides several key advantages, chief among them the ability to perform large-scale evaluations (we consider 101 temporal VLMs in this analysis) as well as support for precisely controlling key parameters of the input dataset. We follow up our synthetic evaluations with additional analyses on real-world settings in Section
4.1 Designing Evaluation Framework
In this section, we introduce our approach for quantitatively evaluating TROVE. Our insight is to predefine static feature b; then, train a temporal VLM such that it learns bias with respect to 6, resulting in classification errors at inference time on class label ÿ. This approach allows us to pair trained VLMs with ground-truth annotations for error-inducing static feature biases b and associated class labels ÿ. Consequently, we can evaluate TROVE by measuring its ability to identify biases that align with the ground-truth annotations. Given this setup, we design suite of evaluation configurations, with each configuration consisting of the following components:
1. A vision languaea training dataset with injected predefined static feature bias. The vision- language training dataset = {(S,, T consists of multi-image sequences S; paired with textual captions T: We construct sequences using synthetie images, where each constituent image in S; depicts blue circle placed on a black background. The sequence is paired with a textual caption T, indicating the direction of movement of the circle across the sequence; namely, the circle may be "moving north", "moving south", "moving west", or "moving east".
Given this setup for dataset D, we then select of the following static visual features: (i) background features, where all pixels located outside the circle are colored red, (ii) object features, where a red rectangle is inserted where the color of the circle changed to red. We then insert the selected static visual feature of interest into dataset D such that the feature is highly prevalent in sequences where the circle is "moving south" and low in prevalence otherwise; this procedure injects bias into the training dataset, contributing errors at inference time when the static feature appears in sequences from other classes, Examples of sequences with injected static visual features are provided in Figure 3
random position in the image, and (iii) attribute features,
2. A temporal VLM trained on this dataset. We train a temporal VLM F using training dataset D. Since the dataset exhibits a strong bias with respect to the predefined static feature, model likely to pick up this shortcut during training.

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Table 1: TRoVE reliably demonstrates strong performance across all three feature categories.
Background
Ohject
Attribute P@ 100
Method
P@ 10
P@
P@ 100
R-Prec
P@ 10
P@25
P@ 100
R-Prec
P@ 10
P@
R-Prec
Random
20.7
19.6
18.8
18.3
14.3
15.0
16.3
16.2
16.2
19.1
19.4
18.8
Domino
48.6
47.0
32.4
19.7
52.2
47.1
34.8
23.1
63.1
57.8
35.1
19.1
George
45.5
44.7
42.0
35.6
45.0
41.1
44,8
35.7
46.2
46.8
44.4
38,9
Dist. Failures
62.9
62.0
59,9
49.0
60.4
58.3
55.9
49.9
69.2
68.0
66.4
62.3
Confidence
61.0
60.6
64.6
59.5
97.8
97.0
95.6
78.0
58.5
59.4
57.9
52.5
TROVE
100.0
100.0
100.0
95.0
97.8
97.8
97.8
92.8
100.0
100.0
99.3
89.3
downstream temporal understanding task. We establish a downstream temporal understand- ing task that aligns closely with the training data; namely, given multi-image sequence depicting blue circles in each image, the task involves classifying the motion of the circle as one of four classes: moving north, moving south, moving east, and moving west.
We create a large suite of evaluation configurations by varying the following hyperparameters: (i) the type of static visual feature (background, object, attribute), (ii) the sequence length n,, (iii) the prevalence of sequences in the training dataset with the static feature, and (iv) the number of images per sequence displaying the static feature. We then verify the quality of each configuration by evaluating (i) the suitability of the proposed task and (ii) the suitability of the trained VLM. After the quality verification stage, our framework yields total of 101 temporal VLMs paired with ground-truth annotations indicating the predefined static feature bias b and the downstream class label y which the bias induces errors. Additional details are provided in Appendix A
4.2 TROVE Accurately Discovers Error-Inducing Biases in Synthetic Settings
We now evaluate TROVE using the framework from Section 4.1 We provide the trained VLM F and dataset Dv as input. Then, for each class label in Dv. TROVE outputs list of image clusters ranked by TROVE scores; each cluster represents an identified error-inducing static feature bias.
Recall from Section 4.1 that our framework annotates VLM F with both the ground-trute static feature bias 6 (namely, the red background, red rectangle, or red circle) and the downstream class label ÿ on which the bias induces errors. In order to score the output of TROVE, we compute Precision @ K. defined as the proportion of the top-K images in the generated ranked list for class ū that depict b. In line with prior works on error discovery [24] 277, large Precision @ K values suggest that human user can easily understand the TROVE EeioVeietteeeeed bias by simply inspecting the top-K K returned images.
We compare TROVE with five methods for systematic error detection. Three state-of-the-art ap- proaches for systematic error discovery in non-temporal settings are considered: Domino [24]. George (26). and Distilling Failures (25). Since these methods were designed for non-temporal settings, each input sequence is represented as a single unit; thus, these methods generate ranked lists of sequences as output rather than ranked lists of images. We naively adapt these methods to the temporal setting by first generating a ranking of sequences and then sorting images from each sequence in temporal order. In addition to these methods, we also compare TROVE with a ceriiuuly-developee temporal approach that we refer to as Confidence. Confidence, which is an application of the method proposed in Li al. [66], ranks images from sequences Dy by their maximum image-level prediction confidence. Finally, we consider random baseline, where we pool together images from all sequences in Dv and then generate a random ordering.
Results are summarized in Table [ where we report Precision @K K for K = 10, 25, 100 as well as R-precision, a variant of Precision @ K where K is equal to the total number of images in Dy annotated with the ground-truth static bias. We demonstrate that TROVE outperforms all other evaluated methods, achieving a 28.6% improvement over the closest baseline (namely, confidence). Existing non-temporal systematic eтror detection methods (Domino, George, and Distilling Failures) demonstrate low performance when directly extended to the temporal setting due to their inability to retrieve the specific images containing the static feature from sequence. Across all three static feature categories explored our framework (background, object, and attribute). TROVE demonstrates superior performance compared to the other methods. We note that whereas baselines exhibit significant variations performance across the three static feature categories, TROVE consistently achieves strong performance. Extended results and ablations are provided in Appendix B
7

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Model: VideoCLIP-XL
Analysis of Class Label "sticking tongue
TRoVe
Task: Activity Recognition
Does discovered feature contribute to prediction errors?
Does the discovered faature
represent a learned static bias?
Discovered
Static Feature
Blas
Acouracy
100
Image Lovel Prediction Confidence
sticking tongue out
Samples with
Sampler without baby
Modell BioViL-T
Analysis of Class Label "improving"
TRoVe
Task: Pneumonia Progression
Does discovered contribute to prediction errors?
Does discovered feature represent leerned static bias?
Discovered
33.3
Static Feature
50.0
72.3
Biass
Acouracy
Image Level Prediction Confidence
improving
Samplee with markers severe presmores
Samplos los without monkerrs sovore pricaiionio
Figure 4: [Left Panel] Qualitative examples of static feature biases discovered by TROVE across var- ious temporal VLMs and downstream tasks. [Right Panel] We demonstrate that TROVE-ddscoverre biases satisfy desired properties.
5 Evaluating TROVE in Real- Settings
Given the strong performance of TROVE on our synthetic experimental settings, we now utilize TROVE to discover error-inducing static feature biases learned by real-world temporal VL.Ms. As discussed in Section 4 evaluating the accuracy of discovered biases in real-world settings is challenging. Consequently, we validate PRRVV--ddentffed biases in two ways. First, in Section 1 we utilize image-level pseudolabels to verify that static feature biases surfaced by TROVE exhibit desired properties. Second, in Section[5.2) we demonstrate that knowledge of RRVV-discovvree features can aid with mitigating prediction errors at test time, yielding substantial performance improvements without significant model training or data augmentation. Extended implementation details and results are in Appendix
C and
5.1 TROVE Accurately Discovers Error-Inducing Biases in Real-World Settings
Analyzing Pretrained VLMs with TROVE: We analyze a suite of pretrained contrastive VLMs with temporal understanding capabilities 12
across two temporal understanding tasks 400-class activity recognition on Kinetics400 and 2-class pneumonia progression classification on MS-CXR-T 40 g For the six pretrained VLMs evaluated on activity recognition, TROVE identifies between 36 and 116 learned static feature biases per model. For the one pretrained VLM evaluated on pneumonia progression classification, TROVE identifies learned static feature biases.
In Figure 4 (left panel), we provide examples of static feature biases and associated class labels surfaced by TROVE. For the VideoCLIP-XL model, TROVE surfaces a feature cluster consisting of babies; this suggests that when static features associated with babies are present in a sequence, VideoCLIP-XL is likely to exhibit degraded performance on the class sticking tongue out. Similarly, on pneumonia disease progression classification task, TROVE discovers a cluster of chest X-rays depicting features such as bilateral opacities, medical devices, and low lung volumes, which are indicative of severe pneumonia. This suggests that when a chest X-ray in a multi-image sequence depicts such features, Vio is likely to exhibit degraded performance on the class improving due to a learned static feature bias. We provide additional qualitative examples in Figure
Validating Discovered Biases: In Figure (right panel), we validate the accuracy of biases discov- ered by TROVE. For the activity recognition task, we utilize an open- voccbuccyyulary object detector 44] annotate the presence of babíes all constituent images for sequences with class label sticking tongue out. We find that (i) classification accuracy of VideoCLIP-XI is significantly lower on this class label (by 15.4 points) when babies are present, and (ii) VideoCLIP-XI. demonstrates high prediction confidence when classifying static sequences with babies, suggesting learned bias. Predicted lahels for incorrectly sequences in this class include baby waking up and carrying baby, further corroborating our finding that the model is focusing on the presence of the
8

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baby rather than the true dynamic visual changes associated with the action sticking tongue out. Similarly, for the pneumonia progression classification task, we utilize radiology reports as well as domain-specifie entity extraction tool [45] in order to annotate each image with the presence of markers associated with severe pneumonia (i.e. bilateral lung involvement, devices, and low lung volumes). We find that (i) classification accuracy of Bio ViL.T is lower (by 16.7 points) when these markers are present, and (ii) Bio demonstrates high prediction confidence when classifying static sequences with these markers. Additional validation is provided in Appendix D
Comparing Temporal and Von-Tempora VLMs: Non-temporal VLMs (e.g. CLIP II) can be applied to temporal tasks by encoding each sequence the mean of its constituent image-level embeddings. Intuitively, since non-temporal VLMs are trained only on static image-level features, we should expect to see high rates of statie feature biases. Indeed, we find that this intuition holds. Across four non-temporal VLMs [ 46) evaluated on activity recognition, TROVE discovers an average of 134.5 + 38.8 static feature biases per model. This is considerably larger than the six which exhibit an average of 84.5 27.3 static feature biases per model.
temporal VLMs 12 11 +
5.2 TROVE Improves Downstream VLM Classification Performance
We now demonstrate that knowledge of TROVE- identified static feature biases can aid with mit- igating prediction errors on downstream tasks. We specifically consider contrastive temporal VLMs as case study, which have demonstrated state-of-the performance on many temporal understanding tasks 212
Table 2: We show that classification accuracy of VLMs can be improved given knowledge of TROVE-identifiee static feature biases. This table reports performance (Accuracy @5) on subset of videos in Kinetics400 [40] containing the top 20 static features identified by TROVE.
We first run TROVE validation dataset Dv which generates as output ranked list of im- age clusters (representing learned static feature biases) and associated class labels (on which the presence of the static feature induces errors). Let C represent an identified image cluster, such as the cluster of trees in Figure [ and let ğ repre- sent the associated error-prone class label, such as the label swinging on something in Fig- ure 2 Due to the learned bias, sequences with the static feature represented by are particularly difficult for the VLM to correctly classify.
Model
Label y
Overall
VideoCLIP-XL
51.7
82.2
+ +TROVE
94.4
86.7
VICLIP-B
45.3
73.4
+ TROVE
95.8
77.7
ViCLIP-L
71.4
77.1
+ TROVE
96.9
80.7
Prior works in non-temporal settings have suggested that VLM classification accuracy can be improved by injecting text prompts with additional fine-grained detail in order maximize class- level separation [47 48]. We aim to improve VLM performance on sequences with feature C' by leveraging CoOp, an approach for automatically learning effective prompts 49 We use CoOp to learn prompts that achieve the best possible classification accuracy among sequences in Dy with at least one image in cluster C. All parameters in the VLM are frozen, avoiding significant training costs. At test time, given an input sequence with an unknown label, we first use the trained clustering model from Section to determine the sequence contains at least one image in cluster C. so, we use the learned prompts to perform classification; otherwise, we use default prompts.
We apply our mitigation approach improve the performance of three contrastive temporal VLMs on an activity recognition task (Kinetics400 (40). In Table we report classification performance (Accuracy @5) across test set sequences with least one image assigned to the top-20 TROVE- identified static feature clusters. Across this set (denoted as "Overall" in Table we observe strong performance improvements when applying our mitigation approach. Notably, on sequences this set with ground-truth labels ý that are particularly impacted by static feature biases (denoted as "Label g" Table 2). we observe performance improvements of up to 111 %. We note that the "Label j" and "Overall" categories in Table 2 are analogous to worst-group and average analyses performed robustness literature. Our results show that knowledge of TROV TROVF-identifff biases can aid in improving test-time VLM performance by correcting errors induced by learned static feature biases.

---

Conclusion
In this work, introduced TROVE, automated approach for improving robustness of temporal VLMs. Given temporal VLM, TROVE discovers learned static feature biases that contribute to prediction errors on downstream tasks. Ultimately, our work can help enable users to discover and mitigate important failure modes in temporal VLMs prior to deployment in real-world settings.
Acknowledgments and Disclosure of Funding
MV is supported by graduate fellowship awards from the Knight-Hennessy Scholars program at Stanford University, the Quad program, and the United States Department of Defense (ND- SEG). AC is supported by NIH grants RO1 HL167974, RO1HL169345, RO1 AR077604, RO1 EB002524, RO1 AR079431, P41 EB027060. AY2 AX000045, and LAYS AX0000024-01; ARPA- H grants AY2AX000045 and IAYSAX0000024-01 and NIH contracts 75N92020C000008 and 75N92020C00021. AC has provided consulting services to Patient Square Capital, Chondrometrics GmbH, and Elucid Bioimaging; is co-founder of Cognita; has equity interest in Cognita, Subtle Medical, LVIS Corp, Brain Key. CL is supported by NIH grants RÓ1 HL155410, RO1 HL157235, by AHRQ grant R R18HS026886, and by the Gordon and Betty Moore Foundation. CL is also supported by the Medical Imaging and Data Resource Center (MIDRC), which is funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under contract 75N92020C00021 and through the Advanced Research Projects Agency for Health (ARPA-H).
This research was funded, part, by the Advanced Research Projects Agency for Health (ARPA-H). The views and conclusions contained in this document those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Govermment.
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Appendix
Contents
A Implementation Details for Synthetic Evaluations
16
Extended Results for Synthetic Evaluations
18
Implementation Details for Real-World Evaluations
18
D Extended Results for Real-World Evaluations
21
D.1 Analysis of Pretrained Temporal VLMs
D.2 Improving Downstream Classification Performance
23
D.3 Extending to Additional Tasks
23
Additional Applications of TROVE
25
Extended Related Work
26
Extended Discussion
27
A Implementation Details for Synthetic Evaluations
In this section, expand on Section 4.1 by providing additional implementation details for our evaluation framework.
Implementation details for vision-language training datasets with injected predefined static feature biases: Sequences in the vision-language training dataset D are composed of synthetic images, where each image depicts a blue circle placed on black background. To generate images, we first create empty image of size 60 pixels x 60 pixels. We then place blue circle with diameter of 10 pixels random location on the image. The position of the blue circle will vary across the sequence; specifically, the circle will be "moving north" (towards the upper border of the image), "moving south" (towards the lower border of the image), "moving west" (towards the left border of the image), or "moving east" (towards the right border of the image). Textual captions paired with each sequence indicate the direction the circle's s movement.
As stated in Section 4.1 we create a large suite of evaluation configurations by varying several hyperparameters associated with the training dataset. We define each configuration by selecting single value for each hyperparameter. Hyperparameters and possible values are described in detail below:
Type of static visual feature. Motivated by the types of static feature biases that emerge in real-world settings [16,, we consider three categories of static features: (i) background features, where all pixels outside the circle are colored red, (ii) object features, where red rectangle with dimensions 15 pixels x 15 pixels is inserted a random location, and (iii) attribute features, where the color of the circle is changed red. For object features, we ensure that the red rectangle does not overlap with the blue circle when placed in the image. By design, these three categories of static features vary in visual subtlety, with background features resulting in the most pixel-level changes and attribute features the least.
Sequence length. We consider four options for the sequence length ny: 2 images, 3 images, 5 images, and 10 images.
Prevalence of sequences the training set with the static feature. In line with prior work (50). we use the Cramer's ' V metric ensure that the presence of the static feature in the training set is
16

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strongly associated with the group of sequences in which the circle is "moving south". We consider the following values of Cramer's V: 0.7, 0.8, 0.9, and 0.95.
Number of images per sequence displaying the static feature We select a nonzero integer value U; less than or equal to the value of ng. When injecting the static feature into selected sequence in DD we randomly select a contiguous subsequence of v images to depict the feature. For example, in Figure 3
2 2 and n, 5.
Implementation details for trained temporal VLMs: As part of our evaluation framework, we train a temporal VLM F using training dataset D. Model F is implemented in the form of a simple contrastive VI.M where the vision and text encoders are based on the CLIP ViT-L/14 architecture. For each input sequence S₁, constituent images are passed through the vision encoder followed by a trainable projection head consisting of two linear layers interspersed with a ReLU activation. Our architecture includes total of n& projection heads, and the appropriate projection head for each image is selected based on its position in the sequence. We assume that n; remains constant for all sequences in the dataset. The resulting embeddings for constituent images are concatenated together (in order to preserve temporal information) and then passed through projection head consisting of three linear layers interspersed with ReLU activations. The output of this projection head is a single embedding characterizing the sequence.
Training is performed on single NVIDIA V100 GPU using batch size of 256, an initial learning rate of lc-4, and a total of 100 epochs with early stopping based on validation set performance. All parameters associated with the vision and text encoder remain frozen, whereas parameters associated with the projection heads are learnable. At inference time, classification is performed by computing the cosine similarity between the sequence-level embedding and text embeddings associated with each class; the class with the highest cosine similarity is selected as the prediction.
Implementation details for downstream temporal understanding tasks: The downstream tem- poral understanding task takes the form of classification task in which the motion of the circle must be classified in one of four categories: moving north, moving south, moving east, and moving east. In order to reflect real- world settings, we assume that the dataset Dv used in the downstream task is drawn from the same distribution as the training dataset, where the injected static feature is highly prevalent for the class "moving south" and less prevalent other classes. For each evaluation configuration in our suite, we use the same instantiations of hyperparameters for both the training dataset and the downstream task dataset.
Quality verification: We verify the quality of each evaluation configuration across two axes: (i) the suitability of the proposed task and (ii) the suitability of the trained VLM.
First, in order to evaluate the suitability of the proposed task, we train a temporal VLM on a version of the training dataset with no inserted static feature, and we verify that the downstream inference task can be successfully solved by this model. We also train a standard, non-temporal VLM on this dataset perform the task using only a single selected image per sequence, and we verify that the downstream task cannot be solved by this model; here, we adopt the approach introduced by Buch al. [7]. In combination, this analysis confirms that in an unbiased setting, the task can only be addressed by temporal VLM capable of parsing visual changes across multiple images. In practice, we retain evaluation configurations where the temporal, unbiased VLM exhibits least a 20 point improvement over the non-temporal, unbiased VLM; the remainder are filtered out.
Second, in order to evaluate the suitability of the trained VLM, we verify that (i) the VLM has learned the predefined static feature bias and (ii) the presence of the static feature contributes to mispredictions. To evaluate whether the VLM has learned the predefined static feature bias, we compute the difference in image-level classification performance between images without the predefined static feature and images with the predefined static feature. We retain evaluation settings where the gap in performance is greater than 20 points on at least one class ÿ; the remainder are filtered out. To evaluate whether the presence of the static feature bias contributes to mispredictions, we compute the difference in sequence-level classification performance between sequences without the predefined static feature and sequences with the predefined static feature. Again, we retain all evaluation configurations where the gap in performance is greater than 20 points on class j. In combination, this analysis confirms that the trained VLM has learned the statie feature bias of interest and that the static feature bias contributes to errors at least one class associated with the downstream task.
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Summary statisties: After the quality verification stage, our framework yields a total of 101 valid evaluation configurations, each consisting temporal VLM paired with ground -ruth annotations indicating the learned static feature bias b and the downstream class label i which the bias induces errors. Below, we provide breakdown of these configurations across various factors:
Downstream class label ÿ;: Out of the 101 valid evaluation configurations, the value of ÿ equals moving north for 17 configurations, moving west for 36 configurations, and moving east for 48 configurations.
Type of static visual feature: Out of the 101 valid evaluation configurations, 42 exhibit a background static feature bias, 46 exhibit object static feature bias, and 13 exhibit an attribute static feature bias.
Sequence length: Out of the 101 valid evaluation configurations, 13 have sequences consisting of images, 17 have sequences consisting of images, 34 have sequences consisting of images, and 37 have sequences consisting of 10 images.
Prevalence of sequences the training set with the static feature: Out of the 101 valid evaluation configurations, has Cramer's V score of 0.7, 12 have a Cramer's V score of 0.8, 38 have Cramer's V score of 0.9, and 50 have a Cramer's V score of 0.95.
Proportion of images per sequence displaying the static featuree: Out of the 101 valid evaluation configurations. 25 have between 20% and 49% of the frames per sequence depicting the static feature, 38 have between 50% and 79% of the frames per sequence depicting the static feature, and 38 have between 80% and 100% of the frames per sequence depicting the static feature.
B Extended Results for Synthetic Evaluations
In this section, extend the results provided in Section 4.2 with additional performance breakdowns. In Figure we provide a breakdown TROVE performance by the number of images per sequence. As part of suite of 101 trained VLMs, consider VLMs trained numbers of images n, per sequence; in particular, we consider n4 E {2, 3, 5, 10}. As shown in Figure 5 TROVE demonstrates strong performance across all four categories. On the other hand, see considerable declines in performance for the Confidence baseline as the number of images per sequence increases.
datasets with varying
In Figure 6 we analyze TROVE performance with respect to the proportion of images per sequence displaying the static feature. As part of our suite of 101 trained VLMs, we consider VLMs trained on datasets with varying proportions images per sequence containing the static visual feature; for instance, in sequence consisting of five images, proportion of 0.4 indicates that two images in the sequence display the static feature, as depicted in Figure 3 TROVE demonstrates strong performance across the spectrum, whereas baselines again show considerable variation. The ability of TROVE e operate effectively even when the static feature only visible in a portion of the sequence is a key advantage over the non-temporal systematic error detection methods (Domino, Distilling Failures, and George) evaluated this work.
In Figure 7 we provide overall performance metrics across four evaluation metrics: Precision @ 10, Precision @ 25, Precision 100, and R- R-Precision. Across all metrics, TROVE demonstrates superior performance to baselines, demonstrating that our approach is effective at generating accurate ranked lists of identified static feature biases.
In 3 we provide an ablation with respect to the two components that make up the TRoVE score: the Static Bias Score (SBS) and the Error Contribution Score (ECS). We demonstrate that using both components in tandem yields significantly higher performance across our synthetic evaluations than using SBS alone. We do not perform an ablation with the ECS alone, since using ECS alone will not specifically target static feature biases, which are the topic of this work.
C Implementation Details for Real-World Evaluations
In this section, we provide additional implementation details associated with the analysis in Section
5
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80
Random
Precision@225
60
Domino
George
40
Distilling Fallures
Confidence
20
TRoVe (Ours)
Number of Images per Sequence
Figure 5: TROVE demonstrates strong performance across evaluation configurations with varying numbers of images per sequence.
Random
Precision@25
Domino
George
0.4
Distilling Failurres
TRoVe (Ours)
0.2
0.0
0.2
0.3
0.4
0.5
0.6
0.7
Proportion Images per Sequence Displaying the Static Feature
Figure 6: TROVE consistently demonstrates strong performance regardless of the proportion of images per sequence displaying the static feature.
We utilize TROVE to analyze pretrained contrastive VLMs with temporal understanding capabilities using two temporal understanding tasks: activity recognition on Kinetics400 40 and pneumonia progression classification MS-CXR-T (8)
Activity Recognition Kinetics400: The activity recognition task Kinetics400 involves classifying a video into one of 400 possible classes that represent human actions, such as welding or climbing tree. The model is presented with sequence of video frames input; for our analysis, we use 8 frames per video obtained via uniform sampling. We split the validation set of Kinetics400 into development set (used for the analysis in this section) and test (used for the analysis in Section 5.2 where we mitigate biases). We compute logits and predictions for each model using standard zero-shot classification approach, performed by computing the cosine similarity between the sequence embedding and text embeddings representing each class label. In line with prior work [1]. we compute text embeddings by ensembling the following prompt templates for each class label: ("a photo of [LABEL]."" "a photo of person [LABEL].", "a photo of person using [LABEL].", photo of a person doing [LABEL].", " "a photo of a person during [LABEL].", ". photo of person performing [LABEL]." 'a photo of a person practicing [LABEL].", "a video of [LABEL]." "a video of person [LABEL]." video a person using [LABEL]." video person doing [LABEL].", "a video of person during [LABEL]." "a video of person performing [LABEL]]", "a video of a person practicing [LABEL]]." "a example [LABBEL].". "a example of a person [LABEL].", "a example of person using [LABEL]." ",, example of a person doing [LABEL]." "a example of
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100
-0
160
Proster
20
0
Randon
Domino
Goorge
Disiliin Fallunes
Confidance
TRuke [Duund
Rardon
Donino
Goorge
Disiling Falkras
Conridance
TRine: (Dung)
1.00
80
Prebionn:00
40
20
Domiioo
Gerrge
Diiiliiee
TRaWe
Denaioo
George
Disiling
TRite
Figure 7: We compute four metrics to characterize overall performance of TROVE on our evaluation framework: Precision @ 10, Precision @25, Precision @ 100, and R-Precision.
Table Ablations with respect the two components of the TRoVE score.
Method
P@ 10
P@25
P@100
RP
Static Bias Score (SBS) Only
76.2
76.2
75.9
78.2
Static Bias Score (SBS) Error Contribution Score (ECS)
99.1
99.1
98.9
93.2
person during [LABEL].", "a example person performing [LABEL].", "a example of a person practicing [LABEL].". "a demonstration of [LABEL].", "a demonstration of person [LABEL],", "a demonstration of person using [LABEL].", "a demonstration
a person doing [LABEL].", "a demonstration of person during [LABEL]." ", "a demonstration of a person performing [LABEL].", "a demonstration person practicing [LABEL]."] "). Kinetics400 open-source.
We analyze six contrastive VLMs on the activity recognition task (VideoCLIP-XI XCLIP-B/32 [43), and XCLIP-L/14 4
12
ViCLIP-L
ViCLIP-B [T] XCLIP-B/16
As described
in Section 3 TROVE includes a clustering stage. The optimal number of clusters is selected automatically by sweeping across range of potential values [|>]] * 2, | * 6) increments of 400; here, the bounds of the range evaluate [800, 2400), given the fact that |>]| 400. We classify prediction as correct the ground-truth label appears in the top-5 predicted classes (i.e. Accuracy @ 5). We exclude any identified static feature biases where (1) the error contribution score is low (defined as below predefined threshold of 0.1) and (2) the static bias score is less than or equal to random chance (defined 1/| 0.0025 in this case). VideoCLIP-Xy XL is available under CC-By-NC-SA- -A--4.. ViCLIP and XCLIP are available under MIT licenses. We implement TROVE using single NVIDIA V100 GPU.
Pneumonia Progression Classification on MS-CXR-T: The pneumonia progression classification task on MS-CXR-T involves classifying a sequence of chest X-rays collected at varying timepoints into one of two possible categories: improving, which suggests that pneumonia improving over the course of the sequence, and worsening, which suggests that pneumonia is worsening over the course of the sequence. Each sequence contains two chest X-rays. We compute logits and predictions using standard zero-shot classification approach. In line with prior work 8 we compute text embeddings by ensembling across prompt templates. For the class label improving, we utilize the following prompts: ("pneumonia is better", "pneumonia is cleared". "pneumonia is decreased", "pneumonia decreasing". "pneumonia improved", "pneumonia improving". "pneumonia reduced". "pneumonia is resolved", "pneumonia is resolving", "pneumonia is smaller", }. For the
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class label worsening, we utilize the following prompts: ("pneumonia is bigger", "pneumonia is developing", "pneumonia is enlarged", "pneumonia is enlarging", "pneumonia greater", "pneumonia is growing", .arss"."pppee "pneumonia is increased", "pneumonia is increasing". "pneumonia is larger", "pnnuuoonia new", "pneumonia progressing". "pneumonia progressive", "pneumonia is worse". "pneumonia is worsened", "pneumonia is worsening"). MS-CXR-T is available under PhysioNet Credentialed Health Data License 1.5.0.
We analyze one contrastive VLM on the pneumonia progression classification task (BioViL-T 8 As described in Section 3 TROVE includes a clustering stage. The optimal number of clusters
selected automatically by sweeping across range of potential values [[ * 2. [3/] * 6) at increments
of 1; here, the bounds of the range evaluate any identified static feature biases where (1) the error contribution score is low (defined as below a predefined threshold of 0. 0. and (2) the static bias score is less than or equal to random chance (defined as 1/|y| = 0.5 in this case). BioViL-TT is available under an MIT license. We implement TROVE using a single NVIDIA V100 GPU.
[4, 12). given the fact that [ 2. We exclude
D Extended Results for Real- Evaluations
D.1 Analysis of Pretrained Temporal VLMs
[A]
Analysis of Class Label "pushing cart"
Model: VICLIP-B
TRoVe
Tasic Activity Recognition
Does the discovered feature contribute prediction errors?
Does the discovered feature represent learned static
Discovered
$1.9
Stutic
Accuracy
InageeLaval Predksiion Confislnce
100
Class Lobel
Samplex
[8]
Model: VideoCLIP XL
Analysis of Class Label "swinging some something"
TRoVe
Tasic Activity Recognition
Does the discovered feature contribute to prediction errors?
Does the discovered feature represent learned static blas?
Discooered
50.0
42,8
Stucic
66.7
Acouracy
10o
Images avel Predecino Confidence
Class
swinging
something
Samples mee
Samples
Model: VICLIP-
Analysis Class "sticking tongue out
TRoVe
Tasic Activity Recognition
Does the discovered feature contribute to prediction errors?
Does the discovered feature represent a fearned statio bias?
Discovered
47.1
78.3
Statle Fenturn
75.0
96.7
Blus
Accurity
15c
Imaage socell Predfitiion Conficlence
700
Class Label
sticking toogue out
Samplle with/aty
Sumpler withoutishy
[D]
Model: VICLIP-L
Analysis of Class Label "sonersaul saulting""
TRoVe
Taslc Activity Recognition
Does the discovered festure contribute to prediction errors?
Does the discovered feature represent a
Discovered Statkc Feature
Blos
Apcurecy
10c
Imrages) nevil Predictioon Confieeence
100
Class sonereaulting
with
Figure 8: Additional qualitative examples of static feature biases discovered by TROVE across various temporal VLMs. the activity recognition task, we identify 104 static feature biases for VideoCLIP-XL, 104 static feature biases for ViCLIP-I, 116 static feature biases for ViCLIP-B, 66 static feature biases for XCLIP-B/16, 81 static feature biases for XCLIP-B/32, and 36 static feature biases for XCLIP-L/14. On the pneumonia progression classification task, we identify 4 static feature biases for BioV We
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Does the discovered feature represent a learned static bias? For each constituent image per incorrectly-classfiee sequence, we construct a static sequence consisting of the single image repeated nı times. We then obtain seftmax-nnrrallizee prediction logits when performing classi- fication with the static sequence, and we extract the maximum value across all classes (i.e. the maximum prediction confidence). If the BRoViddisoverre bias is accurate, then we would expect to see large confidence values, suggesting that the model is able to make highly-confiden predic- tions without the use of any temporal information. In particular, we would expect to see confidence values significantly larger than the confidence values that would be expected by random chance (i.e. 1/D)), our plots in Figures f and 8 we provide comparison of the mean image-level feature and images without observe a general trend that as the size of the model increases across the same family (for example, ViCLIP-B vs. ViCLIP-L and XCLIP-B/16 vs. XCLIP-L/14), the number of identified static feature biases decreases, potentially suggesting that larger models are less reliant on static feature biases. This follows logically from the fact that larger models also exhibit better overall performance with lower error rates on the activity recognition task, suggesting that these models are more likely to have learned true dynamic patterns. We also note here that the number of identified static feature biases is strongly correlated with the size of dataset and the number of classes; as a result, the smaller number of identified static feature biases for pneumonia progression classífication in comparison to activity recognition is expected.
We validate the quality of each discovered bias by verifying whether the desired properties (namely, error-inducing and learned model bias) are satisfied. Here, we provided extended details on our comman-n--the-loee validation procedure introduced in Section 5.1 First, given a TROVF-discovenn cluster of images C' and associated error-prone class label ÿ. a human reader annotates the key feature shared among images in cluster C' (e.g. "baby" the activity recognition example provided in Figure Then, for all sequences in the dataset with class label J. we assign image-level pseudolabels indicating whether the buman-annotatee the activity recognition task, image-level pseudolabels are obtained by leveraging open-vocabulary object detector (OwIV2) [44]. which is prompted to detect the presence of the -amaan-nnnotated feature. The feature is considered to be present in image if the confidence of OwIV2 is at least 0.3. For the pneumonia progression classification task, we obtain image-level pscudolabels by leveraging RadGraph-XL 45), domain- entity recognizer, to annotate findings present in each image. Then, given image-level pseudolabels, we evaluate whether the TRoV E-ddettfiied bias satisfies the two desired properties:
feature (e.g. "baby" in Figure is present or absent. For
off-the-shelf
Does the discovered feature contribute to prediction errors? We compute classification accuracy on sequences from class label ÿ with the human-annotated feature as well as sequences from class label I without the human- annotated feature. If the ddisoveieo bias is accurate, then we would expect to see lower performance class label ÿ when sequences contain the human-annotated feature.
maximum the confidence values between images with the feature.
Figure&&provicee provides additional qualitative examples of error-inducing static feature biases and associated class labels discovered by TROVE, well associated validation results. Below, we analyze the results in each panel:
[Panel A]: TROVE discovers cluster of images depicting children and toy carts, suggesting that when features associated with children and toy carts are present in a sequence, ViCLIP-B is likely the class pushing cart. ViCLIP-B mispredicts these samples
to exhibit lower performance as crawling baby and crying baby, suggesting that the model is relying on static features associated with children when making predictions. In order to validate this finding, use OwlV2 44 to annotate the presence of "child with toy" in all constituent images for sequences in class pushing cart. We find that (i) classification accuracy of ViCLIP-B is significantly lower on this class label (23.3 points) when children and toys are present and (ii) ViCLIP-B demonstrates high prediction confidence when classifying static sequences from this class label with children and toys.
[Panel BI: TROVE discovers a cluster of images consisting of trees, suggesting that when static features associated with trees are present in a sequence, VideoCI.P-XIL is likely to exhibit lower performance on the class swinging on something. VideoCLIP-XI. mispredicts these samples cl climbing tree, suggesting that the model is relying on static features associated with trees
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when making predictions. This example is highlighted in Figure 2 In order to validate this finding. we use OwlV2 44 to annotate the presence of a "tree" in all constituent images for sequences in class swinging on something. We find that (i) classification accuracy of VideoCIIP-XI is significantly lower on this class label (16.7 points) when trees are present and (ii) CideoCLIP-XI. demonstrates high prediction confidence when classifying static sequences from this class label with trees. We note that the difference in mean image-level maximum confidence values between images with trees and images without trees is relatively small in this example (3 points); regardless, the prediction confidence is substantially larger than what would be expected by random chance (0.25 42.8), suggesting that the model is relying on a learned static bias.
[Panel C]: TROVE discovers cluster of images consisting of babies, suggesting that when static features associated with babies are present in a sequence, ViCLIP-L is likely to exhibit lower performance on the class sticking tongue out. ViCLIP-L mispredicts these samples baby waking up, suggesting that the model is relying on static features associated with babies when making predictions. Interestingly, this example is similar to the example provided in Figure 4 (left panel), suggesting that ViCLIP-L and VideoCLIP-X both learned a similar error- arror-inuucing static feature bias. In order to validate this finding, "baby" in all constituent images for sequences in the class sticking tongue out. We find that (i) classification accuracy of ViCLIP-L is significantly lower this class label (27.9 points) when babies are present and (ii) ViCLIP-L demonstrates high prediction confidence when classifying static sequences from this class label with babies.
OwIV2 44 to annotate the presence of
[Panel D]: TRoVE discovers cluster of images consisting of divers with swimming pool back- grounds, suggesting that when these static features are present in a sequence, ViCLIP-L is likely to exhibit lower performance samples are the class labels springboard diving and jumping into pool, suggesting that the model is relying we note here that although these predictions do not align with the ground-truth label (somersaultin))
the class comersaulting Frequent mispredictions in this set of
static features associated with the pool when making predictions. Importantly,
they are not necessarily incorrect in this setting: this raises the possibility of another potential use case for TROVE in real-worid settings: flagging potential labeling errors. To validate the discovered bias, we use OwIV2 for sequences in the class somersaulting, We find that (i) classification accuracy of ViCLIP-L is significantly lower on this class label (13.5 points) when pools are present and (ii) ViCLIP-L demonstrates high prediction confidence when classifying static sequences from this class label with pools.
4 to annotate the presence of "pool" in all constituent images
For our analysis of non-temporal VLMs on the activity recognition task, we consider four models:
CLIP- ViTB/22 M1 CLIP-ViTL/4 M. CLIP-RN50 (1. and SigLIP [46]. We identify 195 static feature biases for CLIP- ViTB/32, 92 static feature biases for CLIP- ViTL/14, 111 static feature biases for CLIP-RN50, and 140 static feature biases for SigLIP.
D.2 Improving Downstream Classification Performance
We implement CoOp 49 using the default settings provided in the original implementation. For each considered cluster C, we utilize an SGD optimizer with a learning rate of 0.002 and train for 20 epochs. In Table 2, we reported mean classification accuracy across the test set sequences containing the top-20 TRoV E-identified static features. In Table 4, we extend these results by reporting mean classification performance (evaluated with Accuracy @ 5) across the test set sequences containing the top-5 TROVE E Eidennifiee static features and the top- 10 TRoVE-identifee
static features. In Table 5 we report Accuracy @1 metrics. Table lists the number of test set sequences considered under each category when computing results in Tables 2 4 and 5 Our mitigation approach leads to consistent
performance improvements with minimal computational cost, demonstrating the practical utility of Eddeetiidd biases.
D.3 Extending to Additional Tasks
We now extend our analysis on activity recognition to two additional tasks: 600-class activity recognition on Kinetics600 (5T) and 174-class fine-grained activity recognition on Something- Something V2 (52). Here, we specifically consider the VideoCLIP-XL model [12].
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Table 4: We show that classification accuracy of VLMs can be improved given knowledge of TRoVE- identified static feature biases. This table reports performance (Aceuracy@e @) on subset of videos in Kinetics400 [40] containing the top-5 TROV i-ddentiieee static features, the top-10 TROVE e-idenideteeeeed static features, and the top-20 TRoVe-identified static features.
Model
Top Identified Features
Top 10 Identified Features Overall
Top 20 Identified Features Label ø
Label ø
Overall
Label
Overall
VideoCLIP-XI.
47.1
84.1
63.0
82.3
51.7
82.2
TROVE
82.4
89.4
91.3
84.7
94.4
86.7
ViCLIP-B
21.1
69.5
22.7
69.0
45.3
73.4
+ TROVE
89.5
75.9
93.2
75.5
95.8
77.7
ViCLIP-L
58.6
69,9
64.3
75.7
71.4
77.1
+ROVE 96.6
78.2
96.4
78.8
96.9
80.7
Table 5: We show that classification accuracy of VLMs can be improved given knowledge of T- identified static feature biases. This table reports performance (Accuracy @ 1) on a subset of videos in Kinetics400 [40] containing the top-5 static features, and the top-20 TROV E-iientiffee static features.
static features, the top-10 TROVE-identinn
Model
Top 5 Identified Features
Top 10 Identified Features Overall
Top 20 Identified Features
Label y
Overall
Label
Label ø
Overall
VideoCLIP-XL
17.6
48.3
21.7
52.7
18.0
55.0
TROVE
41.2
58.5
45.7
55.8
61.8
57.1
ViCLIP-B
5.3
34.2
6.8
37.7
24.2
43.1
TROVE
15.8
43.9
50.0
47.9
62.1
50.9
VICLIP-L
3.4
35.2
16.1
40.9
23.5
45.6
+ TROVE
48.3
46.1
53.6
48.2
63.3
50.1
We first analyze the performance of VideoCLIP-XX XL on Kinetics600 using TROVE. In this setting, TROVE identifies 149 learned static feature biases; in comparison, TROVE had identified 104 learned static feature biases on Kinetics400. Intuitively, the larger number of static feature biases discovered with Kinetics600 is expected due to the fact that Kinetics600 is an approximate superset of Kinetics400.
We find high consistency between results Kinetics400 and Kinetics600. For instance, the top- ranked static feature identified by TROVE on Kinetics600 depicts cluster of trees paired with the class label "swinging on something", suggesting that when static features associated with trees are present in sequence, VideoCLIP-XX is likely to exhibit lower performance on the class "swinging on something". identical static feature bias was identified when evaluating VideoCLIP-XL on Kinetics400, shown in Figures | 2 and g [Panel BJ. The ability of TROVE to yield consistent results across two distinct evaluation settings demonstrates
reliability. TROVE also uncovers new static feature biases not identified in Kinetics400, such as a link between static features associated with trampolines and errors on the class label "backflip (human)".
We then analyze VideoCLIP-XX on Comething-Somethiee lenging task zero-shot settings due the fine-grained nature of class labels (e.g. "putting something on surface", "pushing something from left to right", etc.). VideoCLIP-XX XL achieves an overall zero-shot performance (Accuracy@55 5) just 18.1 on VideoCLIP-XL with TRoVe, we discover 14 error- inducing static feature biases in this setting. The low number of identified static feature biases in comparison to Kinetics400 and Kinetics600 aligns with expectations, since Something-Something V2 was specifically designed to ensure that reliance on single-frame, static content will not aid with predicting any of the classes; thus, models are unlikely to rely static features shortcuts this task. Consequently, the observed low zero-shot
V2. Something-Somechingg V2 is a chal-
V2. When analyzing

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Table 6: Here, we list the number of test set sequences considered under each category when computing results in Tables and The "Overall" column lists the number of test set sequences with at least one constituent mmge assigned to the top-K TRRoV-identiffee static feature clusters. The "Label D" column lists the number of test set sequences with ground-truth label ÿ and least one constituent image assigned to the top-K TROVE- E-ieentiiee static feature clusters.
Model
Top Identified Features
Top 10 Identified Features
Top 20 Identified Features
Label ù
Overall
Label y
Overall
Label ø
Overall
VideoCLIP-XI.
17
207
46
419
89
669
ViCLIP-B
187
44
355
95
831
ViCLIP-L
29
193
56
452
98
866
Table 7: We show that classification accuracy of VLMs on Kinetics600 and Something-Sometiine V2 can be substantially improved given knowledge of T--ienidentiffed static feature biases.
Model
Kinetics600 Label
Something-Something V2
Overall
Label y Overall
VideoCLIP-XI.
54.4
80.1
30.0
+ROVE
97.1
85.1
97.6
50.5
performance of VideoCLIP-XL. Nomething-Something V2 is likely result of general reasoning limitations rather than learned static feature biases.
Next, we extend the analysis provided in Section 5.2 by mitigating prediction enrors Kinetics600 and Something-Somethingg V2 at test time. Table shows that classification accuracy of VideoCLIP- XL on these tasks can be substantially improved given knowledge of TROV E-identified static feature biases.
E Additional Applications of TROVE
In this section, we highlight another potential use-case of TROVE in real-world settings: analyzing the composition of temporal datasets. To analyze the composition of Kinetics400 [40, we construct an ensemble of six contrastive temporal VLMs; then, for each model, we use TROVE to identify static feature biases and corresponding class labels on which the bias induces emors. Our analysis finds that two classes Kinetics400, namely sneezing and cracking neck, are identified as error-prone classes by all six models; this finding suggests that these two classes are likely to be consistently impacted by learned static feature biases. In contrast, 185 classes like training dog and dancing ballet are not affected by learned crror-inducing static feature biases for any of the considered models.
In Figure 9 we vary the models included in the ensemble. l [Panel A] considers an ensemble of two ViCLIP models (ViCLIP-B and ViCLIP-L) and identifies a total of 33 classes that are consistently impacted by learned static feature biases across both models. The 33 identified classes (sorted by their mean TROVE scores) include: hockey stop, sticking tongue out, sneezing, blowing nose, dining, ski jumping, shaking head, kicking field goal, blasting sand, texting, biking through snow, passing American football (not in game), water sliding, waxing legs, slapping. tasting food, waxing chest, skateboarding, cating burger, shining shoes, whistling, making tea, robot dancing, cracking neck, cating spaghetti, shuffling cards, baking cookies, surfing crowd, egg hunting, bending back, opening bottle, tapping pen, and jumping into pool. Figure 9 [Panel B] considers ensemble of three XCLIP models ECCLI-B/11. XCLIP-B/32, and XCLIP-L/14) and identifies total classes that are consistently impacted by learned static feature biases across all three models. The 4 identified classes include: making a cake, sneezing, cating chips, and cracking neck.
Ultimately, given temporal dataset associated with a task of interest, TROVE can enable identifi- cation of challenging class labels that are particularly susceptible to systematic errors from static feature biases, aiding with model development and evaluation.
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[A]
Kinetic4400 Classes Consistently Impacted by Static Feature Blases (VICLIP model family)
TRoVe Score
Class Label
BB]
Kinetics400 Classes Consistently Impacted by Static Feature Blases (XCLIP model family)
TRoVe Score
Class Label
Figure 9: Several classes in Kinetics400 found be consistently impacted by learned static feature biases across multiple trained VLMs. Panel [A] depicts Kinetics400 classes consistently impacted by static feature biases ViCLIP-L and ViCLIP-B. Panel [B] depicts Kinetics400 classes consistently impacted by static feature biases across XCLIP-B/16, XCLIP-B/32, and XCLIP- L/14.
F Extended Related Work
Here, we extend Section by providing a discussion on the temporal action localization (TAL) task, which involves trimming videos only include frames in which the action is directly visible [53]. TAL is useful preprocessing step for reducing the effects of noisy or irrelevant frames and be helpful in some cases for mitigating the influence of static feature biases; however, that TAL is not a universal solution for the following two reasons. First, in activity recognition settings, static features static features are often directly observed in relevant action frames. As a representative example, consider Figure (Panel A), where TRoVE discovers that the presence of children and toy carts leads to prediction errors on the class "pushing cart". The learned static feature bias directly involves the subject performing the action, and trimming content will be useful for addressing these prediction errors. Second, existing TAL methods are designed specifically for activity recognition and cannot be easily extended to other temporal settings. our work, we extend beyond the centtionally-essaarche human activity recognition setting and include evaluations on both synthetic task and medical imaging task. TAL methods cannot be easily extended to these domains, as there is no clear analog to "trimming" content. Thus, TAL will have effect on the presence of static feature biases here. As result, static feature biases cannot solved by only trimming videos to discard noisy or irrelevant frames, and methods like TROVE are necessary for identifying this important failure mode.
simply limited noisy irrelevant frames; rather, error-inducing
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G Extended Discussion
Potential Societal Impacts: In this work, we demonstrate that static feature biases are a critical issue for temporal VLMs. Static feature biases are type of spurious correlation and can contribute to prediction errors on downstream prediction tasks. We hope that our proposed approach can be utilized to detect and mitigate errors resulting from static feature biases prior to real-world deployment. Such an approach has the potential to improve robustness temporal VLMs. This can be particularly advantageous in safety-critical settings like healthcare, where models capable of processing medical images across multiple timepoints are gaining increasing popularity.
Limitations and Future Work: Our work aims to discover and mitigate error-inducing static feature biases learned by temporal VLMs. We specifically focus on image sequences in this work, where each sequence represents a series of images collected over time (c.g. video frame sequences, medical images collected varying timepoints, etc.). However, temporal data exists in many other modalities. such as audio and signals. Our work does not consider these settings, and determining the role of static feature biases in inducing prediction errors across non-image modalities would be an interesting direction for future work.
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Reviews:
[{"review_id": "H6k4mstcmu", "reviewer": "Reviewer_mPEn", "summary": "This paper introduces TROVE, an automated method for identifying static feature biases in temporal VLMs. These biases can degrade model performance in real-world temporal understanding tasks. TROVE analyzes predictions on validation data, clustering static features and scoring them based on their contribution to errors and the model‘s reliance on them. Evaluated on both synthetic and real-world datasets, TROVE outperforms prior baselines and reveals previously unknown biases in existing VLMs. Experiments on real-world benchmarks, including Kinetics-400 for activity recognition and MS-CXR-T for pneumonia progression classification, demonstrate TROVE’s effectiveness in improving the robustness of temporal VLMs in practical applications.", "questions": "* It is suggested to include a discussion comparing TROVE with the temporal action localization (TAL) task, which also captures temporal dynamics and may naturally mitigate static feature biases. Clarifying this distinction would help justify the necessity of the proposed approach. The misclassified video shown in Figure 1 lacks the action subject (the person) in the last two frames, which appears to result from untrimmed video content. Notably, TAL is specifically designed to handle untrimmed videos, and a comparison with TAL could provide valuable insights.\n\n* To enhance the generalizability and robustness of the findings, additional evaluations on diverse video datasets such as Kinetics-600 and Something-Something V2 are recommended. The latter, in particular, poses stronger temporal reasoning challenges. The proposed method relies on annotations from the validation set. Is it possible to achieve similar results using only training set annotations?\n\n* The evaluation framework would benefit from ablation studies on its key components, including the clustering strategy and scoring mechanism, to better validate the design choices and their contributions.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### Strengths\n\n* The paper provides a thorough analysis of the impact of static features on temporal VLMs, which holds practical significance for real-world applications.\n\n* It proposes a comprehensive evaluation framework that includes 101 trained temporal VLMs paired with ground-truth annotations for learned static feature biases.\n\n* The method is validated on multiple VLMs, and the paper presents detailed explanations that enhance readability and support reproducibility.\n\n### Weaknesses\n* The paper focuses on static feature biases in temporal settings but does not discuss how existing tasks like temporal action localization, which already identify the start and end times of actions, can inherently reduce the influence of static features. This lack of discussion raises questions about the necessity of the proposed approach.\n\n* The evaluation in the paper is based on a limited set of datasets. It would be more convincing to validate the method on additional video datasets such as Kinetics-600 and Something-Something V2, especially the latter, which involves more dynamic visual changes.\n\n* The evaluation framework lacks sufficient ablation studies to verify the effectiveness of its individual components.", "was_revised": true, "final_justification": "My concerns have been resolved. Considering the overall quality of the paper and the opinions of the other reviewers, I believe a borderline acceptance is the most appropriate recommendation.", "rebuttal": "We thank Reviewer MPEN for reviewing our work and providing helpful feedback.\n\n> **[Q1] Comparison with Temporal Action Localization (TAL). “It is suggested to include a discussion comparing TROVE with the temporal action localization (TAL) task, which also captures temporal dynamics and may naturally mitigate static feature biases. Clarifying this distinction would help justify the necessity of the proposed approach. The misclassified video shown in Figure 1 lacks the action subject (the person) in the last two frames, which appears to result from untrimmed video content.”**\n\nThank you for this suggestion. The TAL task, which involves trimming videos to only include frames in which the action is directly visible, is certainly a useful preprocessing step for reducing the effects of noisy or irrelevant frames when performing activity recognition. However, TAL is not a universal solution for mitigating the effects of static feature biases for the following reasons:\n- **In activity recognition settings, static features are not simply limited to noisy or irrelevant frames; rather, error-inducing static features are often directly observed in relevant action frames.** As a representative example, consider Figure 8 (Panel A), where TRoVe discovers that the presence of children and toy carts leads to prediction errors on the class \"pushing cart\". The learned static feature bias directly involves the subject performing the action, and trimming content will not be useful for addressing these prediction errors. In the example from Figure 1 where the sequence is misclassified as \"climbing tree\", there are indeed several frames without the human subject in frame; however, we observe that frames in this sequence with the subject in frame demonstrate more evidence of the learned error-inducing static bias (max image-level prediction confidence of 93.0 on class label \"climbing tree\") than frames without the subject in frame (max image-level prediction confidence of 9.8 on class label \"climbing tree\").\n- **Existing TAL methods are designed specifically for activity recognition and cannot be easily extended to other temporal settings.** A key advantage of our approach is its ability to operate across diverse temporal settings; to this end, we extend beyond the traditionally-researched human activity recognition setting and include evaluations on both a synthetic task and a medical imaging task. TAL methods cannot be easily extended to these domains, as there is no clear analog to \"trimming\" content. Thus, TAL will have no effect on the presence of static feature biases here.\n\nIn summary, static feature biases are a challenging and well-studied problem that cannot be solved by only trimming videos to discard noisy/irrelevant frames. We will update the related works section in the final version of our manuscript to clarify these distinctions.\n\n> **[Q2] Additional Evaluations. “To enhance the generalizability and robustness of the findings, additional evaluations on diverse video datasets such as Kinetics-600 and Something-Something V2 are recommended. The latter, in particular, poses stronger temporal reasoning challenges.”**\n\nIn our submitted manuscript, we utilized TRoVe to analyze a suite of temporal VLMs across three representative tasks: 4-class movement direction classification on synthetic data, 400-class activity recognition on Kinetics400, and 2-class pneumonia progression classification on MS-CXR-T. In response to your suggestion, we have extended our evaluations to include two additional temporal understanding tasks: 600-class activity recognition on Kinetics600 and 174-class fine-grained activity recognition on Something-Something V2 (SSV2). Key takeaways from these evaluations are discussed below.\n\n**Takeaway 1: TRoVe discovers learned static feature biases that contribute to errors on Kinetics600 and SSV2.** We first use TRoVe to analyze the performance of VideoCLIP-XL on Kinetics600. In this setting, TRoVe identifies 149 learned static feature biases; in comparison, in our submitted manuscript (Section 5.1), TRoVe had identified 104 learned static feature biases on Kinetics400. Intuitively, the larger number of static feature biases discovered with Kinetics600 is expected due to the fact that Kinetics600 is an approximate superset of Kinetics400. \n\nWe find high consistency between results on Kinetics400 and Kinetics600. For instance, the top-ranked static feature identified by TRoVe on Kinetics600 depicts a cluster of trees paired with the class label \"swinging on something\", suggesting that when static features associated with trees are present in a sequence, VideoCLIP-XL is likely to exhibit lower performance on the class \"swinging on something\". An identical static feature bias was identified when evaluating VideoCLIP-XL on Kinetics400, as shown in Figure 1, Figure 2, and Figure 8 [Panel B]. The ability of TRoVe to yield consistent results across two distinct evaluation settings demonstrates its reliability. TRoVe also uncovers new static feature biases not identified in Kinetics400, such as a link between static features associated with trampolines and errors on the class label \"backflip (human)\".\n\nWe then analyze VideoCLIP-XL on SSV2. SSV2 is a challenging task in zero-shot settings due to the fine-grained nature of class labels (e.g. \"putting something on a surface\", \"pushing something from left to right\", etc.). VideoCLIP-XL achieves an overall zero-shot performance (Accuracy@5) of just 18.1 on SSV2. When analyzing VideoCLIP-XL with TRoVe, we discover 14 error-inducing static feature biases in this setting. The low number of identified static feature biases in comparison to Kinetics400 and Kinetics600 aligns with expectations, since SSV2 was specifically designed to ensure that reliance on single-frame, static content will not aid with predicting any of the classes; thus, models are unlikely to rely on static features as shortcuts on this task. Consequently, the observed low performance of VideoCLIP-XL on SSV2 is likely a result of general reasoning limitations rather than learned static feature biases.\n\n**Takeaway 2: Knowledge of TRoVe-discovered features can aid with improving VLM classification performance on both Kinetics-600 and SSV2.** We now extend the analysis provided in Section 5.2 by demonstrating that knowledge of TRoVe-identified static feature biases can aid with mitigating prediction errors on Kinetics600 and SSV2 at test time. The tables below show that classification accuracy of VideoCLIP-XL on these tasks can be substantially improved given knowledge of TRoVe-identified static feature biases. \n\nKinetics600:\n|              | Label $\\tilde{y}$ | Overall |\n|--------------|-------------------|---------|\n| VideoCLip-XL | 54.4              | 80.1    |\n| +TRoVe       | **97.1**              |**85.1**    |\n\nSSV2:\n\n|              | Label $\\tilde{y}$ | Overall |\n|--------------|-------------------|---------|\n| VideoCLip-XL | 7.3               | 30.0    |\n| +TRoVe       |**97.6**              | **50.5**    |\n\n\nSimilar to Table 2 in the submitted manuscript, the tables above report performance (Accuracy@5) on a subset of videos in Kinetics600 and SSV2 containing static features identified by TRoVe. The column labels “Label $\\tilde{y}$” and “Overall” are analogous to worst-group and average analyses typically performed in robustness literature. Our results demonstrate similar trends to those observed on Kinetics400 in Section 5.2.\n\n> **[Q3] Use of Validation Set. “The proposed method relies on annotations from the validation set. Is it possible to achieve similar results using only training set annotations?”**\n\nGiven a trained temporal VLM, our approach is designed to be executed as an evaluation step, with the goal of auditing the trained model and informing users about learned static feature biases that contribute to systematic prediction errors. Thus, due to the fact that TRoVe is intended to serve as an evaluation tool, an annotated validation dataset is critical in order to provide an accurate picture of model performance and enable identification of prediction errors induced by learned biases. A training dataset will not be sufficient for this purpose, since (i) utilizing the same dataset for both training and evaluation is not standard practice in machine learning and (ii) a training dataset will not provide an accurate depiction of errors that may occur when deployed on real-world temporal understanding tasks. We also note that the use of annotated validation sets is in line with prior work in the field of systematic error discovery (e.g. [1,2,3]).\n\n[1] Jain et al. “Distilling Model Failures as Directions in Latent Space.” ICLR 2023.\n\n[2] Eyuboglu et al. “Domino: Discovering Systematic Errors with Cross-Modal Embeddings.” ICLR 2022. \n\n[3] Menon et al. “DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers.” EMNLP 2024.\n\n> **[Q4] Ablations. “The evaluation framework lacks sufficient ablation studies to verify the effectiveness of its individual components…The evaluation framework would benefit from ablation studies on its key components, including the clustering strategy and scoring mechanism, to better validate the design choices and their contributions.”**\n\nOur submitted manuscript included ablations in Appendix Section B, which we have reproduced below. Ablations are performed using our 101 synthetic evaluation configurations.\n\n| SBS | ECS | P@10 | P@25 | P@100 | RP   |\n|-----|-----|------|------|-------|------|\n| ✓   |     | 76.2 | 76.2 | 75.9  | 78.2 |\n| ✓   | ✓   | 99.1 | 99.1 | 98.9  | 93.2 |\n\nOverall, we observe that the two components of TRoVe in combination enable accurate discovery of error-inducing static feature biases.\n\nWe again thank Reviewer MPEN for their review of our manuscript. We hope that the above responses adequately address all concerns."}, {"review_id": "v33NpdT4P7", "reviewer": "Reviewer_ZmxL", "summary": "This paper proposes an automated approach (TRoVE) for discovering error-inducing static feature biases learned by temporal VLMs. To quantitatively evaluate TRoVE, this paper builds an evaluation framework consisting of 101 temporal VLMs trained on synthetic data. The evaluation results show that TRoVE can accurately discover error-inducing static feature biases", "questions": "According to line 387, the learned prompts are used if the sequence contains at least one image in cluster C. What if the sequence contains images that are assigned to different clusters?", "limitations": "This paper has discussed its limitations and potential negative societal impact in Section F.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. This paper proposes an automated approach for discovering the learned static feature biases in pre-trained temporal VLM.\n2. This paper build a framework for quantitatively evaluating TRoVE.\n\nWeaknesses:\n1. The main purpose of this paper is to discover the learned static feature biases in pre-trained temporal VLMs. However, the paper does not present possible applications of the TROVE score, such as evaluating temporal VLMs and methods that focus on mitigating the influence of static features. Moreover, in my opinion, mitigating the influence of static features is more important than just discovering them.\n2. The paper shows that TRoVE improves downstream VLM classification in Section 5.2. However, it is unclear whether VideoCLIP-XL is fine-tuned on the downstream classification task. If not, I think the comparison is not fair, as “+TRoVE” is trained on the downstream classification task to learn prompts.", "was_revised": true, "final_justification": "My concerns have been addressed.", "rebuttal": "We thank Reviewer ZMXL for reviewing our work and providing helpful feedback.\n\n> **[Q1] Applications of TRoVe.**\n\nTRoVe is useful in a range of downstream applications; in particular, our work explores three key use-cases of TRoVe.\n\n**Application 1 - Comparing temporal VLMs**. A key benefit afforded by TRoVe is the ability to enable comparisons across models with respect to learned static feature biases. We refer the reviewer to Appendix D, where we provide an extended, systematic analysis of static feature biases discovered across the seven evaluated temporal VLMs. Two key findings from this analysis are: \n\n- **Temporal VLMs exhibit variations in the number of learned static feature biases:** On the activity recognition task, we identify 104 static feature biases for VideoCLIP-XL, 104 for ViCLIP-L, 116 for ViCLIP-B, 66 for XCLIP-B/16, 81 for XCLIP-B/32, and 36 for XCLIP-L/14. On the pneumonia progression classification task, we identify 4 static feature biases for BioViL-T. We observe a general trend that as the size of the model increases across the same family (for example, ViCLIP-B vs. ViCLIP-L and XCLIP-B/16 vs. XCLIP-L/14), the number of identified static feature biases decreases, potentially suggesting that larger models are less reliant on static feature biases. This follows logically from the fact that larger models also exhibit better overall performance with lower error rates on the activity recognition task, suggesting that these models are more likely to have learned true dynamic patterns.\n\n- **Different temporal VLMs may be affected by similar error-inducing static feature biases:** As an example, in Panel C of Figure 8, we apply TRoVe to discover static feature biases learned by ViCLIP-L. TRoVe discovers a cluster of images consisting of babies, suggesting that when static features associated with babies are present in a sequence, ViCLIP-L is likely to exhibit lower performance on the class “sticking tongue out”. ViCLIP-L mispredicts these samples as “baby waking up”, suggesting that the model is relying on static features associated with babies when making predictions. Interestingly, this example is similar to the example provided in Figure 4 (left panel), suggesting that ViCLIP-L and VideoCLIP-XL both learned a similar error-inducing static feature bias.\n\n**Application 2 - Analyzing the composition of temporal datasets.** TRoVe is useful for analyzing the composition of datasets associated with downstream temporal understanding tasks; in particular, TRoVe can identify challenging class labels that are likely to be impacted by learned static feature biases as well as easier classes that are not susceptible to such errors. We refer the reviewer to Appendix E, where we discuss this application of TRoVe and rank class labels in Kinetics400 by TRoVe scores. Key findings are highlighted below: \n\n- **Findings from TRoVe that generalize across multiple temporal VLMs can provide insights into model capabilities as well as the downstream task of interest:** We construct an ensemble of six contrastive temporal VLMs; then, for each model, we use TRoVe to identify learned static feature biases and corresponding class labels on which the bias induces errors. Our analysis finds that two classes in Kinetics400, namely \"sneezing\" and \"cracking neck\", are identified as error-prone classes by all six models; this finding suggests that these two classes are likely to be consistently impacted by learned static feature biases. In contrast, 185 classes like \"training dog\" and \"dancing ballet\" are not affected by learned error-inducing static feature biases for any of the considered models, suggesting that these may be easier classes. We also refer the reviewer to Figure 9, where we extend this analysis by varying the models included in the ensemble. By analyzing findings from TRoVe that generalize across multiple temporal VLMs, we can identify challenging class labels that are particularly susceptible to systematic errors from static feature biases.\n\n**Application 3 - Mitigating prediction errors resulting from static feature biases.** In Section 5.2, we leverage a lightweight prompt-tuning strategy to demonstrate that knowledge of TRoVe-identified static-feature biases can improve classification accuracy, particularly on sequences that belong to challenging, error-prone subgroups. We refer the reviewer to [Q3] below as well as Appendix D.2, which provide additional discussion of how downstream classification performance can be improved using TRoVe.  \n\n> **[Q2] Need for discovery methods. “...mitigating the influence of static features is more important than just discovering them.”**\n\nWe agree that mitigating the influence of static features is an important research direction; however, we emphasize that discovering such biases is also critical for the following reasons.\n\n- **By discovering learned static biases, methods like TRoVe can provide a user with actionable and interpretable insights into critical model failure modes prior to real-world deployment.** Our work extends a long line of research that has aimed to audit models and identify systematic error patterns, ranging from techniques like GradCam that leverage humans-in-the-loop to identify error patterns to more recent fully-automated strategies. The unifying goal behind such methods is to enable users to understand when and why a model is failing, an aim that is particularly important in safety-critical settings like medicine. \n\n- **Methods like TRoVe for discovering static feature biases can support the creation of better mitigation tools.** For example, constructing a two-stage pipeline involving discovery and mitigation (i.e. where knowledge of discovered static feature biases is directly utilized by mitigation algorithms) can make it possible to precisely target specific classes of prediction errors. Additionally, methods like TRoVe can also enable more precise evaluation of mitigation approaches; in particular, knowledge of learned static feature biases can allow users to evaluate performance of mitigation approaches directly on the most challenging data subgroups. We demonstrate these  empirically in Section 5.2.\n\n> **[Q3] Clarification on Downstream Classification. “...it is unclear whether VideoCLIP-XL is fine-tuned on the downstream classification task.”**\n\nThank you for raising this point. VideoCLIP-XL is not fine-tuned on the downstream classification task; rather, we leverage its zero-shot capabilities when classifying videos in Kinetics400. In response to your suggestion, we have incorporated a new baseline in Section 5.2 which we refer to as \"VideoCLIP-XL + Prompt-Tuning on Full Dataset\". The three compared methods in our mitigation evaluations are described in detail below: \n\n- VideoCLIP-XL: We use the VideoCLIP-XL model with default prompts to perform zero-shot classification of test set sequences. \n- VideoCLIP-XL + Prompt-Tuning on Full Dataset: We use CoOp to learn prompts that achieve the best possible classification accuracy using the entire dataset $\\mathcal{D}_V$.\n- VideoCLIP-XL + Prompt-Tuning on TRoVe-Identified Cluster: We use CoOp to learn prompts that achieve the best possible classification accuracy among sequences in $\\mathcal{D}_V$ with at least one image in TRoVe-identified cluster $C$. \n\nWe extend Table 2 below to include the new baseline. In line with the methodology described in Section 5.2, we report \"Overall\" and \"Label \\tilde{y}\" performance, which is analogous to average and worst-group analyses.\n\n|                                   | Label $\\tilde{y}$ | Overall |\n|-----------------------------------|-------------------|---------|\n| VideoCLIP-XL                      | 51.7              | 82.2    |\n| VideoCLIP-XL + Prompt-Tuning on Full Dataset | 52.8              | 82.1    |\n| VideoCLIP-XL + Prompt-Tuning on TRoVe-identified Cluster              | 94.4              | 86.7    |\n\nAs shown in the table above, we observe that full prompt-tuning over the entire dataset $\\mathcal{D}_V$ is insufficient for effectively correcting errors induced by learned static feature biases. On the other hand, knowledge of TRoVe-identified static feature bias helps the VLM to accurately classify sequences that belong to challenging, error-prone subgroups. \n\n> **[Q4] Sequences with Images Assigned to Different Clusters. “According to line 387, the learned prompts are used if the sequence contains at least one image in cluster C. What if the sequence contains images that are assigned to different clusters?”**\n\nOur approach for mitigating prediction errors at test time (Section 5.2) operates as follows. Let cluster $C$ represent a static feature identified by TRoVe, such as the cluster of trees in Figure 2. Our aim is to prevent this static feature from inducing errors at test-time.\n\nTo this end, given an input sequence with an unknown label, we divide the sequence into constituent images and utilize the trained clustering model from Section 3 to assign each image to a cluster. We note that it is possible (and in fact, highly likely) for constituent images in the sequence to be assigned to different clusters; however, if at least one image in the sequence is assigned to cluster $C$, then the static feature associated with cluster $C$ is likely present in the sequence. This means that the sequence is likely to be particularly difficult for the VLM to correctly classify due to the learned bias, so as a result, we use the learned prompts to perform classification. \n\nOn the other hand, if none of the constituent images in the sequence are assigned to cluster $C$, then the static feature associated with cluster $C$ is likely not present in the sequence and the VLM is not likely to be affected by the learned static feature bias when performing classification; in this case, we use default prompts.\n\nWe again thank Reviewer ZMXL for their review of our manuscript. We hope that the above responses adequately address all concerns."}, {"review_id": "4afPgOWgR2", "reviewer": "Reviewer_Ah4m", "summary": "This paper presents TROVE, an automated method to discover error-inducing static feature biases in temporal vision-language models, so as to mitigating the potential shortcut during prediction. It extracts candidate static features from validation data and scores them via an Error Contribution Score (ECS) and a Static Bias Score (SBS). Experiments show TROVE outperforms baselines by 28.6% on synthetic data and improves real-world model performance by up to 111%.", "questions": "Please see the **Weaknesses**", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. The paper addresses a crucial and interesting problem. Unlike prior approaches designed for non-temporal tasks, TROVE is tailored for temporal vision-language models, ensuring that the prediction of the VLMs is accurate and robust to open-world scenarios.\n\n2. The proposed approach is reasonable and easy-to-implement.\n\n3. The paper constructs a large-scale synthetic evaluation with 101 VLMs and ground-truth bias annotations, enabling quantitative validation of TROVE's accuracy and generalizability across feature types (background, object, attribute).\n\n**Weaknesses:**\n1. **Clustering dependency on hyperparameters**: The optimal number of clusters in TROVE relies on the Silhouette score, which may require manual tuning for complex real-world datasets. This could introduce variability in bias detection across different domains. \n\n2.  **Computational overhead for large datasets**: Extracting image-level embeddings and performing clustering on large validation datasets (e.g., Kinetics400) incurs high computational costs, which are not explicitly optimized in the current framework.\n\n3. **Lack of cross-model generalization analysis**: The paper evaluates TROVE on 7 VLMs but does not systematically analyze how well biases detected on one model generalize to others, limiting insights into transferability across different architectures.", "was_revised": true, "final_justification": "The authors have addressed my concerns. I will maintain my original positive score.", "rebuttal": "We thank Reviewer AH4M for reviewing our work and providing helpful feedback.\n\n> **[Q1] Clustering hyperparameters. “The optimal number of clusters in TROVE relies on the Silhouette score, which may require manual tuning for complex real-world datasets. This could introduce variability in bias detection across different domains.”**\n\nIn order to identify static features that occur consistently within the dataset, TRoVe involves a clustering step. As with any clustering approach, the number of clusters is a required hyperparameter; however, in order to avoid manual tuning of this hyperparameter, we instead leverage an automated approach for selecting the optimal number of clusters. Specifically, we sweep across a range of potential values [$|\\mathcal{Y}|$*2, $|\\mathcal{Y}|$*6) at fixed intervals. Here, $|\\mathcal{Y}|$ represents the number of class labels associated with the downstream task. We then identify the optimal number of clusters based on Silhouette scores. \n\nBy setting the bounds with respect to the number of classes, we utilize a straightforward approach for automatically selecting this hyperparameter that is robust to dataset complexity. We show that this approach works effectively across datasets of varying complexity without manual tuning; for example, in Figure 5, we demonstrate strong performance on  synthetic evaluations even as the length of the sequence increases (i.e. the data becomes more complex). We also show efficacy across two real-world tasks from varying domains where the value of $|\\mathcal{Y}|$ differs substantially (2 classes vs. 400 classes). Finally, we note that our automated approach for selecting the optimal number of clusters is in line with prior work in the field of systematic error discovery (e.g. [1]). \n\n[1] Sohoni et al. \"No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems\". NeurIPS 2020.\n\n> **[Q2] Computational overhead for large datasets. “Extracting image-level embeddings and performing clustering large validation datasets (e.g., Kinetics400) incurs high computational costs, which are not explicitly optimized in the current framework.”**\n\nTRoVe is designed to operate with minimal computational costs. In most cases, dataset $D_v$ generally takes the form of a small validation dataset, where TRoVe can operate without substantial computational overhead. When dataset $D_v$ is large, such as in the case of Kinetics400, the process of generating image-level embeddings constitutes the bulk of TRoVe's (still minimal) computational cost. Empirically, we observe embedding generation to take between 0.5 and 2 seconds per video in Kinetics400 depending on the VLM being used for generation. In our work, we found a single NVIDIA V100 GPU to be sufficient for running all evaluations involving TRoVe. Given the embeddings, the clustering and scoring procedure incur minimal computational costs; in particular, both clustering and scoring can be run on CPU and can benefit from parallelization. We implement clustering using the faiss package in Python in order to maximize computational efficiency. On Kinetics400, clustering image-level embedding takes between 25 seconds and 1 minute (where time is proportional to the number of clusters). The scoring stage takes less than 5 seconds. \n\n> **[Q3] Cross-model generalization analysis. “The paper evaluates TROVE on 7 VLMs but does not systematically analyze how well biases detected on one model generalize to others, limiting insights into transferability across different architectures.”**\n\nThank you for this suggestion. We agree that a key benefit afforded by TRoVe is the ability to enable comparisons across models with respect to learned static feature biases. We refer the reviewer to Appendix D and Appendix E of our submitted manuscript, where we provide an extended, systematic analysis of the static feature biases discovered across the seven evaluated VLMs. Some key findings are highlighted below:\n- **Temporal VLMs exhibit variations in the number of learned static feature biases:** On the activity recognition task, we identify 104 static feature biases for VideoCLIP-XL, 104 static feature biases for ViCLIP-L, 116 static feature biases for ViCLIP-B, 66 static feature biases for XCLIP-B/16, 81 static feature biases for XCLIP-B/32, and 36 static feature biases for XCLIP-L/14. On the pneumonia progression classification task, we identify 4 static feature biases for BioViL-T. We observe a general trend that as the size of the model increases across the same family (for example, ViCLIP-B vs. ViCLIP-L and XCLIP-B/16 vs. XCLIP-L/14), the number of identified static feature biases decreases, potentially suggesting that larger models are less reliant on static feature biases. This follows logically from the fact that larger models also exhibit better overall performance with lower error rates on the activity recognition task, suggesting that these models are more likely to have learned true dynamic patterns.\n- **Different temporal VLMs may be affected by similar error-inducing static feature biases:** As an example, in Panel C of Figure 8, we apply TRoVe to discover static feature biases learned by ViCLIP-L. TRoVe discovers a cluster of images consisting of babies, suggesting that when static features associated with babies are present in a sequence, ViCLIP-L is likely to exhibit lower performance on the class “sticking tongue out”. ViCLIP-L mispredicts these samples as “baby waking up”, suggesting that the model is relying on static features associated with babies when making predictions. Interestingly, this example is similar to the example provided in Figure 4 (left panel), suggesting that ViCLIP-L and VideoCLIP-XL both learned a similar error-inducing static feature bias.\n- **Findings from TRoVe that generalize across multiple temporal VLMs can provide insights into model capabilities as well as the downstream task of interest:** We construct an ensemble of six contrastive temporal VLMs; then, for each model, we use TRoVe to identify learned static feature biases and corresponding class labels on which the bias induces errors. Our analysis finds that two classes in Kinetics400, namely \"sneezing\" and \"cracking neck\", are identified as error-prone classes by all six models; this finding suggests that these two classes are likely to be consistently impacted by learned static feature biases. In contrast, 185 classes like \"training dog\" and \"dancing ballet\" are not affected by learned error-inducing static feature biases for any of the considered models. We also refer the reviewer to Figure 9, where we extend this analysis by varying the models included in the ensemble. Figure 9 [Panel A] considers an ensemble of two ViCLIP models (ViCLIP-B and ViCLIP-L) and identifies a total of 33 classes that are consistently impacted by learned static feature biases across both models. Figure 9 [Panel B] considers an ensemble of three XCLIP models (XCLIP-B/16, XCLIP-B/32, and XCLIP-L/14) and identifies a total of 4 classes that are consistently impacted by learned static feature biases across all three models. This analysis demonstrates that by analyzing findings from TRoVe that generalize across multiple temporal VLMs, we can identify challenging  class labels that are particularly susceptible to systematic errors from static feature biases; these results can aid with model development, model evaluation, and downstream task design.\n\nWe again thank Reviewer AH4M for their review of our manuscript and their positive overall assessment of our work. We hope that the above responses adequately address all concerns."}, {"review_id": "bB4SwZ6Rk5", "reviewer": "Reviewer_i6UE", "summary": "TROVE is an automated method for identifying static feature biases—such as background or object features—that temporal VLMs rely on instead of dynamic visual changes. These biases can cause systematic errors. TROVE works by extracting candidate static features from a validation dataset and scoring them based on their correlation with model errors and the model’s reliance on them. It achieves strong performance in synthetic evaluations and real-world models, and it can improve test-time performance without retraining by mitigating identified biases using learned prompts.", "questions": "1. The two criteria—being error-inducing (ECS) and reflecting learned bias (SBS)—are treated separately, but it’s unclear whether they are truly independent. If a feature induces errors, doesn’t that imply reliance? Please clarify the distinction and necessity of both.\n2. Appendix B only shows SBS-only results. ECS-only results are missing, making it hard to assess each component’s standalone contribution. Please include ECS-only results.\n3. Consider listing the main contributions explicitly in the introduction for better clarity and structure.", "limitations": "NA", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The problem of static-feature error is critical and the paper is well-motivated.\n2. The paper is organized clearly with pinpointed solutions to open problems.\n3. The experiments include both synthetic and real settings, which has diversity. \n\nWeaknesses:\n1. There are two criteria when discovering the static feature bias, error-inducing and reflecting learned model bias. However, it is a bit unclear whether the two points are independent or not.  \n        (1) Based on my understanding, if the feature already introduces prediction errors, it can indicate that the model relies on this feature for prediction. Explanation should be provided. \n        (2) Ablation study in Appendix B for the two criteria is incomplete. ECS only experimental results is missing. \n2. It is better to list all contributions of the paper in the introduction. Moreover, it is clearer to add citations to the method name in Table 1 and 2.", "was_revised": true, "final_justification": "My concerns are well-addressed. I will keep my positive rating.", "rebuttal": "We thank Reviewer I6UE for reviewing our work and providing helpful feedback.\n\n> **[Q1] Clarification on two components of TRoVe score. “There are two criteria when discovering the static feature bias, error-inducing and reflecting learned model bias. However, it is a bit unclear whether the two points are independent or not. (1) Based on my understanding, if the feature already introduces prediction errors, it can indicate that the model relies on this feature for prediction. Explanation should be provided. (2) Ablation study in Appendix B for the two criteria is incomplete. ECS only experimental results is missing.”** \n\nThank you for raising this question. The first component of the TRoVe score - the error contribution score (ECS) - measures the association between a visual feature and misclassifications; a high ECS score indicates that the visual feature frequently occurs in sequences that the model misclassifies. The second component of the TRoVe score - the static bias score (SBS) - measures model reliance; a high SBS score explicitly indicates that the model relies on that visual feature when making predictions, as measured through analysis of the model’s prediction logits. The two metrics are complementary but independent: \n\n- A high ECS score alone indicates that the feature is associated with misclassifications, but does not guarantee that the model actively *uses* the feature (i.e. the association between the feature and the misclassifications could be purely coincidental). \n\n- A high SBS score alone reveals that the model relies on the feature when making predictions, but this may not necessarily lead to systematic prediction errors. \n\nIn order to effectively illustrate the distinction between ECS and SBS, we extend the ablation in Appendix B. Across a sample of synthetic evaluation configurations, we modify the downstream temporal understanding task (component 3 in Section 4.1) as follows. First, we extract sequences where the pre-defined static visual feature is present; then, with probability p=0.75, we insert a large blue triangle into the last image in the sequence. The motivation behind this simple modification to the downstream task is to draw a clear distinction between (i) features that are often present in mispredicted cases yet do not represent learned static feature biases (i.e the blue triangle); (ii) features that are often present in mispredicted cases and do represent learned static feature biases (i.e. the pre-defined static feature); and (iii) features that are not linked with mispredicted cases and are not learned static biases (i.e. the blue circle). Results (summarized in the table below) show that both the ECS and SBS metrics are necessary to consistently identify the error-inducing static feature bias.\n\n| SBS | ECS | P@10  | P@25  | P@100 | RP   |\n|-----|-----|-------|-------|-------|------|\n| ✓   |     | 75.7  | 75.7  | 75.7  | 79.1 |\n|     | ✓   | 94.6  | 94.6  | 94.6  | 95.1 |\n| ✓   | ✓   | 100.0 | 100.0 | 100.0 | 95.9 |\n\nTo illustrate further, we examine one of the evaluated configurations in detail. Here, the VLM has learned a static feature bias associated with red backgrounds, which is impairing performance on the class label \"moving east\". Our clustering mechanism extracts several clusters of features; in particular, cluster 6 consists predominantly of images with red backgrounds (specifically 1420 images with red backgrounds and 1 image with a black background) and cluster 8 consists predominantly of images with inserted blue triangles (specifically 174 images). Cluster 6 is assigned a ECS score of 0.688 and an SBS score of 0.718, and cluster 8 is assigned a ECS score of 0.987 and a SBS score of 0.377. Although cluster 8 has the higher ECS score, the low SBS score suggests that the model is not necessarily using these features when making the prediction; in other words, the blue triangles are unlikely to represent a learned static bias. When combining the ECS and SBS scores to generate the TRoVe score, cluster 6 comes out on top, suggesting that the red backgrounds are both a learned static feature bias as well as closely associated with systematic prediction errors. \n\nWe will update our paper to include this analysis.\n\n> **[Q2] Listing contributions. “It is better to list all contributions of the paper in the introduction. Moreover, it is clearer to add citations to the method name in Table 1 and 2…Consider listing the main contributions explicitly in the introduction for better clarity and structure.”** \n\nThank you for this suggestion. We will update our introduction with the following list of key contributions:\n- Technical Contribution 1: We introduce TRoVe, **an automated approach for discovering error-inducing static feature biases learned by temporal VLMs**. Given a trained VLM and an annotated validation dataset associated with a downstream classification task, TROVE extracts candidate static features from the dataset and scores each feature by (i) the effect of the feature on classification errors as well as (ii) the extent to which the VLM relies on the feature when making predictions. Knowledge of such static feature biases can enable a developer to better understand and address model failure modes prior to real-world deployment. \n- Technical Contribution 2: Performing quantitative evaluations of automated bias discovery approaches in the temporal setting is complicated by the fact that the ground-truth static feature biases of pretrained models are typically unknown. To address this challenge, we design an evaluation framework consisting of 101 temporal VLMs trained on synthetic data; we pair each VLM with annotations for ground-truth error-inducing static feature biases. To the best of our knowledge, this is the first **large-scale, annotated evaluation testbed** designed to quantitatively assess whether automated methods can accurately discover static feature biases learned by temporal VLMs.\n\nOur technical contributions in this work lead to the following key results. First, across our evaluation testbed with 101 models, TRoVe accurately discovers error-inducing static feature biases, achieving a 28.6% improvement over the closest baseline. Second, we also apply TRoVe to real-world settings; across a suite of 7 off-the-shelf VLMs and 2 temporal understanding tasks, TRoVe accurately surfaces previously-unknown static feature biases. Finally, we show that knowledge of TRoVe-discovered biases can aid in improving model performance at test time. We will make our codebase and evaluation framework publicly available.\n\nCitations for methods in Tables 1 and 2 are currently provided in the body of Sections 4.2 and 5.2. We will update the tables with these citations as well.\n\nWe again thank Reviewer I6UE for their review of our manuscript and their positive overall assessment of our work. We hope that the above responses adequately address all concerns."}]

Author rebuttal:

Meta-review: (a) Scientific Claims and Findings:
The paper introduces TRoVe, an automated method for discovering error-inducing static feature biases in temporal vision-language models (VLMs). It proposes a novel evaluation framework with 101 synthetic VLMs and demonstrates significant improvements over baselines. The method is validated on real-world datasets, showing its effectiveness in identifying biases and improving model performance.

(b) Strengths:
Addresses a critical problem in temporal VLMs.
Provides a comprehensive evaluation framework.
Demonstrates strong performance in synthetic and real-world settings.
Offers practical applications in improving model robustness.

(c) Weaknesses:
Limited discussion on how existing tasks like temporal action localization can mitigate static feature biases.
Limited evaluation on diverse datasets.
Lack of detailed ablation studies.
Unclear independence of error-inducing and learned bias criteria.

(d) Reasons for Decision:
The paper presents a novel and effective approach for identifying static feature biases in temporal VLMs. The comprehensive evaluation framework and significant improvements over baselines are notable strengths. While there are some limitations, the authors have addressed most concerns in their rebuttal, and the overall contributions outweigh the weaknesses.

(e) Discussion and Changes During Rebuttal:
Reviewers raised concerns about the necessity of the approach, dataset limitations, and ablation studies.
Authors provided additional evaluations on Kinetics600 and Something-Something V2, demonstrating consistent results.
Authors clarified the distinction between error-inducing and learned bias criteria and provided additional ablation studies.
Authors addressed the computational overhead and clustering hyperparameters, showing robustness across datasets.
Decision: Accept (poster)