Measuring Faithful and Plausible Visual Grounding in VQA
Authors: Daniel Reich
Felix Putze
Tanja Schultz
Venue: EMNLP 2023 Findings (2023)
Keywords: Visual Grounding
Visual Question Answering

Abstract: Metrics for Visual Grounding (VG) in Visual Question Answering (VQA) systems primarily aim to measure a system's reliance on relevant parts of the image when inferring an answer to the given question. Lack of VG has been a common problem among state-of-the-art VQA systems and can manifest in over-reliance on irrelevant image parts or a disregard for the visual modality entirely. Although inference capabilities of VQA models are often illustrated by a few qualitative illustrations, most systems are not quantitatively assessed for their VG properties. 
We believe, an easily calculated criterion for meaningfully measuring a system's VG can help remedy this shortcoming, as well as add another valuable dimension to model evaluations and analysis. 
To this end, we propose a new VG metric that captures if a model a) identifies question-relevant objects in the scene, and b) actually relies on the information contained in the relevant objects when producing its answer, i.e., if its visual grounding is both "faithful" and "plausible". Our metric, called Faithful \& Plausible Visual Grounding (FPVG), is straightforward to determine for most VQA model designs.

We give a detailed description of FPVG and evaluate several reference systems spanning various VQA architectures. Code to support the metric calculations on the GQA data set is available on GitHub.

Measuring Faithful and Plausible Visual Grounding in VQA
Daniel Reich, Felix Putze, Tanja Schultz
Cognitive Systems Lab., University of Bremen, Germany (dreich, felie..utzee tanja.sscheee scw.lschhuhhcccccs wetpejennnbbeeeeeee -bremen. de
Abstract
Q: What is the man in the suil holding?
Palevest
Metries for Visual Grounding (VG) in Visual Question Answering (VQA) systems primarily aim to measure a system's 's reliance on relevant parts of the image when inferring an answer to the given question. Lack of VG has been common problem among state-of-the-an VQA systems and manifest in over-reliance ir- relevant image parts or a disregard for the visual modality entirely. Although inference capabili- ties of VQA models are often illustrated by few qualitative illustrations, most systems are not quantitatively assessed for their VG proper- ties. We helieve, easily calculated criterion for meaningfully measuring a system's VG can help remedy this shortcoming, as well add another valuable dimension to model evalua- tions and analysis. To this end, we propose new VG metric that captures if model a) identifies question-relevant objects in the scene, and b) actually relies on the information con- tained in the relevant objects when producing its answer, i.e,, if its visual grounding is both "faithful" and "plausible". Our metric, called Faithful & Plausible Visual Grounding (FPVG), straightforward to determine for most VQA model designs.
Oicedss
Cojects
VQA Model
Aal: cel phone
Aval: cel phone
Aat: cel phone
Airref
Figure I: Faithful & Plausible Visual Grounding: The VQA model's answer given all objects in the image (Aau) should equal answer when given only relevant objects w.r.t. the question (An.)), and should differ when given only irrelevant objects (Ajrrat). The figure shows model's behavior question deemed faithfully and plausibly grounded.
question and plausible to humans. Hence, visually grounded inference in VQA can be broken down into two aspects: (1) Image contents impact the inference process, and (2) inference based on relevant image contents. Evidence of problematic behavior that arises from lack of (1) includes an over-reliance on language priors (Goyal et al., 2017; Agrawal et al., 2018, 8, 2016), while lack of (2) can cause models to react to changes in ir- relevant parts of the image (Gupta et al., 2022). Both characteristics can hurt model's capacity provide consistent and reliable performances.
We give a detailed description of FPVG and evaluate several reference systems spanning various VQA architectures. Code to support the metric calculations on the GQA data set is available on GitHub!.
1 Introduction
Metrics that quantify model's VG characteris- tics aim to capture its internal reasoning process based on methods of model explanation. These explanations generally vary in properties of plausi- bility and faithfulness. Plausible explanations of a model's behavior prioritize human interpretability, e.g., by illustrating clear inference path over rele- vant objects that lead to the decision, but might not accurately reflect a model's actual decision maaisiinnmaiing process. Faithful explanations, on the other hand,
Visual Question Answering (VQA) is the task of answering natural language questions about image contents. Visual Grounding (VG) in VQA mea- sures a VQA system's inherent proclivity to base its inference on image regions referenced in the given question and relevant to the answer. A well- grounded system infers an answer to given ques- tion by relying image regions relevant to the hhtt:://gghubbccom//tiiccss//

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prioritize a more accurate reflection of a model's decision-making process, possibly at the expense of human interpretability. Examples of plausible ex- planation methods are attention mechanisms (Bah- danau et al., 2014) over visual input objects, and multi-task objectives that learn to produce infer- ence paths without conclusive involvement in the main model's answer decision (Chen et al., 2021). Faithful explanation methods may employ testing schemes with modulated visual inputs followed by comparisons of the model's output behavior across test runs (DeYoung et al., 2020; Gupta et al., 2022). While the latter types of metrics are par- ticularly suited for the use-case of object-based visual input in VQA, they often a) require large compute budgets to evaluate the required number of input permutations (e.g. SwapMix (Gupta et al., 2022), Leave-One-Ous (Li al., 2016)); b) might evaluate in unnecessary depth, like in the case of softmax-score-basee evaluations (DeYoung et al., 2020); and/or c) evaluate individual properties sep- arately and without considering classification con- texts, thereby missing the full picture (De Young et al., 2020; Ying et al., 2022), see also $3.4).
of plausible & faithful VG in VQA.
Evaluations and comparisons of VQA models of various architectural designs with FPVG. New evidence for a connection between VG and OOD performance, provided by an empir- ical analysis using FPVG.
Code to facilitate evaluations with FPVG.
Related Work
Various metrics have been proposed to measure VG in VQA models. We roughly group these into direct and indirect methods. 1) Direct methods: The most widely used methods measuring the im- portance of image regions to a given question are based on a model's attention mechanisms (Bah- danau et al., 2014), or use gradient-baat -ased sensi- tivities (in particular variants of GradCAM (Sel- varaju et al., 2017)). VG is then estimated, e.g.. by accumulating importance scores over matching and relevant annotated image regions (Hudson and Manning, 2019), or by some form of rank correla- tion (Shrestha et al., 2020). Aside from being inap- plicable to non- won-aatentioon-iie
VQA models (c.g.. symbolic methods like Yi et al. (2018); Mao et al. (2019)), attention scores have the disadvantage of becoming harder to interpret the more attention lay- ers are employed for various tasks in model. This gets more problematic in complex Transformer- based models that have a multitude of attention lay- over the input image (OSCAR (Li et al., 2020; Zhhnng al., 2021), LXMERT (Tan and Bansal, 2019), MCAN (Yu et al., 2019b), MMN (Chen et al., 2021)). Additionally, attention mechanisms have been a topic of debate regarding the faithful- ness of their explanation (Jain and Wallace, 2019; Wiegreffe and Pinter, 2019). Gradient-basse sensi- tivity scores can theoretically produce faithful ex- planations, but require careful choice of technique and implementation for each model individually achieve meaningful measurements in practice (Ade- bayo et al., 2018: Feng et al., 2018), Various works introduce their own VG metric based on attention measurements (e.g., GQA-grounding (Hudson and Manning, 2019), VLR (Reich et al., 2022), MAC- Caps (Urooj et al., 2021)) or GradCAM-basse fea- ture sensitivities (Shrestha et al., 2020; Wu and Mooney, 2019; Selvaraju et al., 2019; Han et al., 2021). 2) Indirect methods: These include methods that measure VG based on a model's predictions un- der particular test (and train) conditions, e.g., with perturbations of image features (Yuan et al., 2021;
In this work, we propose VG metric that is both faithful and plausible in its explanations. Faithful & Plausible Visual Grounding (FPVG) quantifies a model's s faithful reliance on plausibly relevant im- age regions (Fig. 1). FPVG is based on a model's answering behavior for modulated sets of image input regions, similar to other faithfulness met- rics (in particular DeYoung et al. (2020)), while avoiding their above-mentioned shortcomings (de- tails in $3.4). To determine the state-of-the-art for VG in VQA, we use FPVG measure various representative VQA methods ranging from one- step and multi-hop attention based methods, over Transformer-based models with and without cross- modality pre-training, to (neuro-Jsymbolic meth- ods. We conclude this work with investigations into the importunce of VG for VQA generalization re- search (represented by testing), thereby further establishing the value of FPVG as an analytical tool. The GQA data set (Hudson and Manning, 2019) for compositional VQA is particularly suited for our tasks, as it pro- vides detailed inference and grounding information for the majority of its questions.
(OOD)
Contributions. Summarized as follows:
A new metric called "Faithful & Plausible Visual Grounding" (FPVG) for quantification

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Gupta et al., 2022; Agarwal et al., 2020; Dououng al., 2020; Alvarez-Melis and Jaakkola, 2017), or specially designed Out-of-Distribution test sets that can inform us about a model's ' insufficient VG prop- erties (Agrawal et al., 2018; Kervadec et al., 2021; Ying et al., 2022). FPVG is related to De Youougg et al. (2020) in particular and uses perturbations of image features to approximate a direct measure- ment of VG w.r.t relevant objects in the input image. Thus, we categorize FPVG as an "indirect" VG evaluation method.
"aa (1dtan))) "san ESau
(1)
Srat (0 Stre Cret
(2)
Siorell (a) S ESrrel
(3)
The relevance of an object in is determined by its degree of overlap with any of the objects referenced in relevance annotations for each indi- vidual question (for details, see App. A). FPVG is then calculated on a data point basis (i.e., for each question) as
Finally, we note that VG can be considered sub-problem of the VQA desiderata gathered under the term "Right for Right Reasons" (RRR) (Ross al., 2017: Ying et al., 2022). RRR may addition- ally include investigations of causal behavior in model that goes beyond (and may not be strictly dependent on) VG and may involve probing the model for its robustness and consistency in expla- nations, e.g., via additional (follow-up) questions (Patro et al., 2020; Selvaraju et al., 2020; Ray et al., 2019; Park al., 2018).
FPVG,= BGGBBB(aaaaaa Asma ) -Bqlaia a Ssree) (4)
where àj is the model's predicted answer for sam- ple 8j and Eq(a, y) function that returns True for equal answers. FPVG takes a binary value for each data point. A positive FPVG value for sample jjat is only achieved if MvQa's output answers are equal between test runs with samples $ia and
Jeat and unequal for samples $jau and Sjeval (re- minder, that the three involved samples only differ in their visual input). The percentage of "good" (i.e., faithful & plausible) and "bad" FPVG is then given as FPVG+ and FPVG respectively:
3 Faithful & Plausible Visual Grounding
3.1 Metric Formulation
We propose new metric to determine the degree Faithful & Plausible Visual Grounding (FPVG) in a VQA model MVQA w.r.t. a given VQA data set S. Here, S consists of tuples sy of question, image and answer (q, i, a)z. Each such tuple in S is accompanied by annotations indicating rele- vant regions in image that are needed to answer the question q. MvQA is characterized by its two modality inputs (i and q) and a discrete answer output (a). In this paper, we expect image to be given as an object-based representation (c.g., bag of objects, scene graph) in line with the de-facto standard for VQA models?.
(5)
FPVG-=1-FPVG,
(6)
We further sub-categorize FPVG to quantify cor- rectly (T) and incorrectly (1) predicted answers jjut as FPVC. and FPVG respec- tively. Hence, samples are assigned one of four categories, following their evaluation behavior (see Fig. 2 for illustration and App. B for the mathe- matical formulation).
3.2 Intuition behind FPVG
The intuition behind the object selections in Syet (relevant objects) and Strret (irrelevant objects) is as follows:
FPVG requires evaluation of Myq4 under three test conditions. Each condition differs in the set of objects representing image in each sample sj of the test. Three tests are run: I) with all available ob- jects (iait, 2) with only relevant objects (Eret), and 3) with only irrelevant objects (Erret)). Formally, we define one dataset variant for each of these three conditions:
Testing on relevant objects Sret. the context of FPVG, the output of a well-grounded system is expected to remain steady for Sret, i.e., the model is expected to retain its original prediction from Salt. if it relies primarily relevant visual evidence. Hence, change in output indicates that the model has changed its focus different visual evidence, presumably away from irrelevant features (which are dropped in S,,) onto relevant features - a sign of "bad" grounding.
"In principle, FPVG can be casily adapted to work with any model (VQA or otherwise) that follows a similar input/output scheme as the standard region-based VQA models, i.c., an input consisting of N entities where a subset can be identified as "relevant" ("irrelevant") for producing a discrete output.

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A: ont phano
FPVG
Augi oall phone
Aval: cell phone
Augi ocll phone I=
Aumel cup
oéjeda
Aui
FPVG,
C: What is the man the sultt holding??
FPVG
Wallee
Angl: wallet
Ausi wallel
I=
Aumei
b) FPVG,
wallet
Augi phone
cell phore
call phone
Aurel sell
o) FPVG.
Aali: call phone
At 090 phore
Ins eva
Arml
sup
cell phone
FPVG.4
Ags: oup A: cell phone
Figure 2: Examples for the four FPVG sub-categories defined in $3.1. Each sub-category encapsulates specific answering behavior for a given question in FPVG's three test cases (Aatt, Arat, Aärret). Categorization depends on grounding status ("FPVG") and answer correctuess ("Ace"). E.g., questions that return a correct answer in Agul and Arat and an incorrect answer in Airrel are categorized as (a). The model's behavior in cases (a) and (b) satisfies the criteria for the question to be categorized as faithfully & plausibly visually grounded.
Testing on irrelevant objects Spreet. In the context of FPVG, the output of a well-grounded system is expected to waver for Spreel, i.e., the model is expected to change its original prediction in Sal, as this prediction is primarily based on relevant visual evidence which is unavailable in Siree--
FPVG and accuracy. FPVG classifies samples jjat C Sall as "good" (faithful & plausible) or "bad" grounding by considering whether or not the changed visual input impacts the model's ' final de- cision, independently of answer correctness. Many VQA questions have multiple valid (non-annotated) answer options (e.g.. "man" vs. "boy" vs. "per- son"), or might be answered incorrectly on account of imperfect visual features. Thus, it is reason- able to expect that questions can be well gell-ggounded, but still produce an incorrect answer, as shown in Fig. 2, (b). Hence, FPVG categorizes samples into two main grounding categories (FPVC. and F PVGL). For more fine- one-grained analysis, an- correctness is considered in two additional sub-categories (FPVGT, FPVG within each grounding category, as defined in Eq. 9-12.
Summarizing expectations for well-grounded VQA. A VQA model that relies on question- relevant objects to produce an answer (i.e., well-grounded model that values visual evidence) should:
1. Retain its answer as long as the given visual information contains all relevant objects.
2. Change its answer when the visual informa- tion is deprived of all relevant objects and consists of irrelevant objects only.
During (1), answer flips should not happen, the model relied only on relevant objects within the full representation Sau. However, due to tendencies in VQA models to ignore visual evidence, lack of flipping in (1) could also indicate over-reliance the language modality (implies indifference to the visual modality). To help rule out those cases, (2) can act as a fail-safe that confirms that model is not indifferent to visual input².
3.3 Validating FPVG's Faithfulness
FPVG achieves plausibility by definition. In this section, we validate that FPVG's sample catego- rization is also driven by faithfulness by verifying that questions categorized as FVG,, are more faithfully grounded than questions in FVVG_. To measure the degree of faithful grounding for each question, we first determine an importance ranking among the question's 's input objects. Then we esti- mate how well this ranking matches with the given relevance annotations. Three types of approaches are used in VQA to measure object importance by direct or indirect means: Measurements of a model's attention mechanism over input objects (direct), graasunt-measuring methods like Grad-
The underlying mechanism can be described as indirect measurement of the model's feature valuation of relevant objects in the regular test S.u. The two additional experimental setups with Syel and Sireel help approximate the measurement relevant feature valuation for Sall-
'Investigations into an alternative formulation of FPVG which ignores requirement (2) can be found in App. C.

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selevant
irrelevant
"Sufficiency" and "comprcheessvvenes" are sim- ilar to FPVG and therefore deserve more detailed comparison. They are calculated as follows.
Method
FPVG, 1 FPVG_
4PVG+
PPVG_1
Attention
60.9
26.6
16.7
51.2
GradCAM 10.4
8.5
53.7
67.4
LOO
29.8
16.0
$2.0
71.7
Definition. Let a model Ma 's answer output layer be represented as nnommax-norrmalzedd logits. A probability distribution over all possible answers is then given as p(alg, fau) = mo fau). The max element in this distribution is My's s predicted answer, i.e., â argmar p(al q. kau), where the
Table 1: Ranking match percentage between feature importance rankings and relevantiirelevant objects for questions in + and FPVG.. Model: UpDn.
CAM (direct), and methods involving input feature manipulations followed by investigations into the model's 's output change (indirect).
probability for the predicted answer is given by Pä.n = Ma lait)a-
VQA-model UpDn's (Anderson et al., 2018) atten- tion and the feature manipulation method Leave- One-Out (LO0*) (Li et al., 2016) were found to deliver the most faithful measurements of feature importance in experiments with UpDn on GQA in Ying et al. (2022). We use these two methods and also include GradCAM used in Selvaraju et al. (2019); Shrestha et al. (2020) for completeness.
Sufficiency is defined as the change of output probability of the predicted class given all objects the probability of that same class given only
relevant objects:
suf -Payet
(7)
Comprehensiveness is defined as the change of output probability of the predicted class given all objects vs. the probability of that same class given only irrelevant objects:
We measure UpDn's behavior on GQA's bal- anced validation set (see $4.1). Table 1 lists the ranking match degree between object importance rankings (based on Sau) and relevance annotations, averaged over questions categorized as FPVG+ and FPVG respectively. The "relevant" ("ir- relevant") category produces high score if all relevant (irrelevant) objects are top-ranked by the used method (see App. D.2 for details). Hence, faithfully grounded questions are expected score highly in the "relevant" category, as relevant ob- jects would be more influential to the model's ' deci- sion.
comp=Pe.at - Pésyrel
(8)
A faithfully grounded model is expected to achieve low values in suff and high values in comp.
Object relevance and plausibility. The defini- tion of what constitutes relevant or irrelevant ob- jects is crucial to the underlying meaning of these two metrics. FPVG uses annotation-driven object relevance discovery and subsequently determines a model's faithfulness w.r.t. these objects. Mean- while, Ying et al. (2022) estimates both metrics using model-based object relevance rankings (e.g.. using LOO), hence, measuring the degree of faith- fulness model has towards model-based valuation of objects as determined by an object importance metric. A separate step is then needed examine these explanations for "plausibility". In contrast, FPVG already incorporates this step in its formula- tion, which determincs if the model's inference is similar to that of a human by measuring the degree of faithful reliance on plausibly relevant objects (as defined in annotations).
Results show that object importance rankings over the same set of questions and model vary greatly across methods. Nonetheless, find that data points in both FPV and FPVG_ achieve on avg favorable scores across all three metrics with mostly considerable gaps between opposing categories (i.e., + and -). This is in line with expectations and confirms that FPVG's data point categorization is driven by faithfulness.
3.4 Comparison with "sufficiency" and "comprehenssveness"
Two metrics measure faithfulness in model, "sufficiency" and "comprehensiveness" were pro- posed in De Young et al. (2020) and used the con- text of VQA in similar form in Ying et al. (2022).
Ad Advantages of FPVG. FPVG overcomes the fol- lowing shortcomings of suf and comp: Suf f and comp are calculated an average over the data set independently of each other and therefore do not evaluate the model for presence of both properties in each data point.
'LOO evaluates a model N times (N=number of input objects), each time "leaving-out-one object" of the input and observing the original answer's ss score changes. A large score drop signifies high importance of the omitted object.

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imrll
suft, comp
100
worst ccomp- best
1.00-
100
1000
0.50
194
7%4
LOO, Ranking Match
80
fipped prediction %
80
80
0.25
0.00
suff
comp
40
-0.50-
-0.75-
20-
X
20
-0.75-0.50--0
0.00 0.25 0.50 0.75 1.00
FPVG. suff
comp FPVC.
best + suff- worst
best worst
FPVG.
FPVG...
Figure 3: Left: Percentage of samples with best (worst) suff & comp scores (medium scores not pictured). Many samples with the fuff property lack comp and vice-versa (gray). Right: LOO-based ranking match per- centages for samples in suff. comp and FPVG (higher is better). Model: UpDn.
Figure 4: Sample distribution and answer class flip per- centages depending on metric categorization. X-axis: VG quality categories based on suf] f & comm (left) and FPVG (right). Y-axis: percentage of flipped answers in each category. Note that this figure, FPVG's formu- lation is interpreted in terms of suff (Eq.. 4, right side, left term) and comp (right term). Model: UpDn.
2. Suff and comp only consider prediction probabilities of the maximum class in isola- tion, which means that even a change in model output as significant as a flip to another class may be declared insignificant by these metrics (c.g., for suff. if the output distribution's ss max probability Pa. is similar to Pa,..)
even though the class prob dropped significantly (>= 40% abs. drop). Both described cases show that failure consider class probs in the context of the full answer class distribution negatively im- pacts the metric's quantification of a model's VG capabilities w.r.t actual effects on its answer out- put behavior. FPVG's categorization avoids this issue by being defined over actual answer changes (Fig. 4, right: flipped prediction percentages per VG category are always at the expected extremes, i.e., 0% or 100%).
Shortcoming 1. Fig. 3, left, illustrates why iso- lating the two properties can cause inaccurate read- ings (1). The analyzed model assigns " "good" suf f scores (defined in Ying al. (2022) as < 1% abs. prob. reduction from Pa.n to Para) to a large number of questions (left two quadrants in Fig. 3, left). However, many of these questions also show "bad" comp (< 20% abs. drop from Pazu to Pāsrrel) (lower left quadrant in Fig. 3, left), which reflects model behavior that one might observe when visual input is ignored entirely. Thus, the full picture is only revealed when considering both properties in conjunction, which FPVG does. Further evidence of the drawback stemming from (1) is pictured in Fig. 3, right, which shows avg LOO-based ranking match percentages (cf. $3.3) for data points cate- gorized "best" suff or comp and FPVG. Data points in FPVG's categories score more favorably than those in sufʃ and comp, illustrating a more accurate categorization.
Summary, FPVG avoids shortcoming (1) by tak- ing both suff and comp into account in its joint formulation at the data point level, and (2) by look- ing at actual answer output changes (Fig. right) and thus implicitly considering class probs over all classes and employing meaningful decision bound- aries for categorization. Additionally, relying on answer flips instead of an abstract softmax score makes FPVG more intuitively interpretable.
3.5 Discussion on other existing metrics
FPVG relies on the method of feature deletions determine "faithful" reliance on "plausible" set of inputs. Other VG metrics exist that instead rely on GradCAM (Shrestha et al., 2020) or a model's 's Attention mechanism (Hudson and Manning. 2019) to provide a "faithful" measurement of input fea- ture importance (see also App. D. The two mentioned metries leverage these mcasurements to determine if a model relies on "plausibly" relevant objects. For instance, Shrestha al. (2020) calcu- lates ranking correlation between the measured GradCAM scores and the rankings based on (plau- sible) object relevance annotations. The metric in
Fig. 4, left, illustrates problem (2). A large percentage of questions with best (=low) scores in suff flip their answer class (i.c., fail to reach 0% flipped percentage), even when experiencing only minimal class prob drops (< 1% abs.). Similarly, some percentage of questions with best (=high) comp scores fail to flip their an- swer (i.e., fail to reach 100% flipped percentage),
Shortcoming 2.

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Hudson and Manning (2019) sums all of model's Attention values assigned visual input objects that have been determined to represent plausible objects.
representation. MAC (Hudson and Manning, 2018) is a multi-hop attention model for multi-step in- ference, well-suited for visual reasoning scenarios like GQA. MCAN (Yu et al., 2019b), MMN (Chen et al., 2021) and OSCAR+ (Zhang et al., 2021) are all Transformer-basee (Vaswani et al., 2017) mod- els. MMN employs modular design that disentan- gles inference over the image information from the question-based prediction of inference steps as a functional program in a separate process, thereby improving interpretability compared monolithic systems like MCAN. MMN also makes an effort to learn correct grounding using an auxiliary loss. OSCAR+ uses large-scale pre-training on multi- ple V+I. data sets and is subsequently fine-tuned on GQA's balanced train set. We use the official release of the pre-trained OSCAR+ base model (which uses proprietary visual features) and fine- tune DFOL (Amizadeh et al., 2020) is a neuro- symbolic method that disentangles vision from lan- guage processing via a separate question parser similar to MMN and VLR (Reich et al., 2022). The latter is modular, symbolic method that prioritizes strong VG over accuracy by following retrieval- based design paradigm instead of the commonly employed classification- design in VQA.
While "plausibility" is straightforwardly achieved by appropriate selection of plausibly relevant reference objects (which would be the same across these metrics), the property of "faithfulness" is more difficult to obtain and heavily dependent on the employed feature importance technique. Investigations in Ying et al. (2022) cast doubt on the faithfulness of GradCAM measurements, with feature deletion techniques and Attention mechanism scoring most favorably in faithfulness in the explored setting. However, discussed in $2, the faithfulness of Attention measurements has not been without scrutiny, and is not straightforwar to extract correctly in models that make heavy use of Attention mechanisms (such as Transformers). Based on this evidence, we find the method of feature deletions to be the most sensible and versatile choice to achieve faithfulness of measurements in FPVG across a wide range of model architectures in VQA.
4 Experiments
In addition to these main models, we include two methods that focus on grounding improve- ments and are both applied to UpDn model training: HINT (Selvaraju et al., 2019) aligns GradCAM- based (Selvaraju et al., 2017) feature sensitivities with annotated object relevance scores. VisFIS (Ying et al., 2022) adds an ensemble of various RRR/VG-related objective functions (including some data augmentation) to the training process.
4.1
Preliminaries
The GQA data set Hudson and Manning (2019) pro- vides detailed grounding information in available train & validation sets. Contrary to HAT (Das et al., 2016), which consists of human attention data for a small percentuge of questions in the VQA data set (Goyal al., 2017). GQA contains automatically generated relevance annotations for most questions in the dataset. Our experiments focus on GQA, but FPVG can theoretically be measured with any VQA data set containing the necessary annotations, like HAT. In this work, we rely on GQA's "bal- anced" split (943k samples), but use the full train split (14m samples) for some models if required in their official training instructions. Testing is performed on the balanced val set (132k samples). Details regarding object relevance determination and model training can be found in App. A and E.
All models, except for OSCAR+, were trained using the same 1024-dim visual features generated by a Faster R-CNN object detector trained on im- ages in GQA using Detectron2 (Wu et al., 2019).
4.3 Evaluations
Results are listed in Table 2, sorted by FPVG_ (last column). Our first observation is that FPVG and accuracy are not indicative of one another, con- firming that our metric for grounding is comple- mentary to accuracy and adds valuable dimension to VQA model analysis. Secondly, we see that (neuro-Jsymbolic methods like DFOL, and VLR in particular, stand out among (non- VG-boosted) VQA models in terms of FPVG, even while trail- ing in accuracy considerably. Thirdly, we find that
4.2 Models
To provide a broad range of reference evaluations with FPVG, we evaluate a wide variety of model designs from recent years: UpDn (Anderson et al., 2018) is an attention- based model that popularized the contemporary standard of object- bbject-bbsed image

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Model
Obj. Det.
Acc
Acraz
FPVG11
FPVG
FPVGLL
FPVGL
FPVG+1
MAC (Hudson and Manning, 2018)
Der2
60.23
59.20
58.1 12
44.33
15.40
7.19
43.81
33.00
22.59
UpDn (Anderson er 2018)
Dec2
57.99
9.68
UpDu-HINT (Selvaraju et 2019)
Det
55.56
57.95
57.88
42,98
16.31
9.72
41.64
32.33
26.03
MCAN (Yu et al., 2019b)
Det2
66.18
65.78
67.3
44.62
20.18
6.20
45.60
28.02
26.37
OSCAR+ (Zhang et al., 2021)
VinVL
70.52
69.96
71.79
50.24
20.37
6.00
49.58
24.05
26.37
MMN (Chen 2021)
Det
68.49
43.93
46.29
DFOL (Amizadeb al., 2020)
Det2
$5.79
57.45
57.36
36.70
20.19
10.03
37.25
32.53
30.22
UpDn=VisF1S (Ying ot ol., 2022)
Det2
$7.09
60.01
63.71
43.25
20.38
12.20
39.63
21.79
32.58
(Reich al., 2022)
Dec2
57.25
UpDn* (Anderson 2018)
VinVI. 65.22
64.81
68.28
43.00
23.90
9.29
40.92
25.89
33.19
Table 2: FPVG results for various models, sorted by F PV Accuracy (Acc) is calculated GQA balanced val set, while all others are calculated on subset (see App. Table for size). Blue arrows show desirable behavior for well-grounded VQA in each category' (hest results in bold). Last line: Results for UpDn* trained with Vin features are included to allow an easier assessment of OSCAR+ (w/ VLn results.
Accorac
FPVG,
answers, hence, a c2i ratio of > reflects that cor- rect answers dominate the considered subset of test questions. In the following analysis, leverage the listed c2i ratios to investigate and illustrate the connection between VG and (OOD) accuracy.
Model
ID
OOD
ID
OOD
UpDn
51.4
30.83-21.96
17,54.87
19.33:173
HINT
51.2811.9
31.34
18.06
19.59
VisFIS
53,28
33.42++1.03
25.1+.78
25.18+.94
MAC
52.14:46
31.31::55
15.4:.51
16.72#222
MMN
52,28
36.48
18.7441.2
17.88
VLR
55.64
$6.38
37,56
38.51
4.4.1
Understanding the connection between FPVG and accuracy.
Table 3: Accuracy (i.e., Accau) and FPVG+ for models evaluated with GQA-101k over five differently seeded training runs.
In Table 2 and we observe a somewhat unpre- dictable relationship between FPVG+ and ac- curacy. We analyze the c2i ratios in Table 4 to gain a better understanding of this behavior. Ta- ble 4 shows that FPVG-curated c2i ratios can vary substantially across model types (e.g., UpDn vs. MMN). These ratios be interpreted as indica- tors of how effectively a model can handle and ben- efit from correct grounding. Large differences be- tween models' c2i profiles explain why the impact of VG on accuracy can vary significantly across models. F.g., MMN has a much stronger c2i pro- file than UpDn, which explains its higher OOD accuracy even with lower F FVG..
methods that boost grounding characteristics, like VisFIS, show promise for closing the gap to sym- bolic methods if not exceeding them. Lastly, we observe that FPV is generally low in all eval- uated models, indicating that there is still ample room for VG improvements in VQA.
4.4 Connection to Dut-of-Distribution (OOD)
We use FPVG to gain insights into the challenge of OOD settings by analyzing VQA models with GQA-101k (Ying al., 2022), dataset proposed for OOD testing. GQA- consists of reparti- tioned train/test set based on balanced GQA and was created following a similar methodology as the OOD split called VQA-CP (Agrawal et al., 2018).
4.4.2 Understanding the connection between FPVG and OOD performance.
The inter-dependency of VG and OOD perfor- mance plays important role in VQA general- ization. FPVG can help gain deeper under- standing.
Results in Table 3 show median values and maximum deviation thereof over five differently seeded training runs per model type (note that VLR uses deterministic inference, so no additional runs were performed for it). Table 4 lists correct-to- incorrect (c2i) answer ratios for six model types trained and evaluated on GQA-101k. The c2i ra- tios are determined for each test set (ID/OOD) and FPVG They are calculated as number of correct answers divided by number of incorrect
More OOD errors when VG is bad. Fig. 5. left, depicts relative c2i ratio degradation when comparing ID to OOD settings. All models suffer a much higher c2i drop for questions categorized as F FPVG_ than FPVG+. In other words, models make more mistakes in an OOD setting in general, but they tend do so in particular when questions are not correctly grounded. Note, that VLR is affected to a much lower degree due to its quasi- insensitivity to Q/A priors.
'FPVG sub-categories F PVG. and T have no intuitively sensible ranking directive under the FPVG motiva- tion.

---

PPVG+
FPVG-
80
Model
00D
ID
OOD
8 70
FPVG,
FPVG.
70
000
60
60
UpDa
1.351.09
.774.05
1.11=.03
A3100
50
50
HINT
1.361.07
.554.05
1.1=-0
01=
ViaFIS
1.4:.06
844.06
1.23-02
.4-00
40
40
MAC
1,442.06
777.00
1.16022
45=00
30
30
MMN
1.914.05
1,214.12
1.11002
-52112
20
20
VLR
1.91
2,12
1.09
1.05
10
degr
20
upDn
HINT
Virrs
MAC
MWN
VLA
upDn
HINT
vons
MAC
MMH
VLR
Table 4: Correct incorrect (c2i) an- swer ratios for questions categorized as -+ Data set: GQA-101k.
Figure 5: Performance drops when comparing ID OOD (questions in + left), and when comparing FPVG+ to FPVG- (questions in ID/OOD, right). Data set: GQA-10Ik.
VG is more important to OOD than ID. Fig. 5, right, shows accuracy sensitivity towards changes in grounding quality, i.e., when compar- ing FVV to FPVG_. We draw two conclu- sions: 1) All models suffer from c2i degradation, hence, they all tend to make more mistakes for ques- tions categorized as FPVG than F 2) This tendency is (considerably) more pronounced in OOD which provides evidence that OOD perfor- mance is particularly sensitive to grounding.
an important role in OOD scenarios, where, com- pared to ID scenarios, bad VG leads to considerably more errors than good VG, thus providing us with a compelling argument for pursuing better- grotud-grouned models.
6 Limitations
Plausibility of explanations in FPVG is assumed to be provided by accurate, unambiguous and com- plete annotations of relevant objects per evaluated question. Although the GQA data set provides an- notations in the shape of relevant object pointers during the inference process for question, these annotations may be ambiguous or incomplete. For instance, question about the color of soccer player's jersey might list pointers to a single player in an image where multiple players are present. Excluding only this one player from the image input based on the annotated pointer would still include other players (with the same jersey) for the Sirvel test case. In such cases, FPVG's assumptions would be violated and its result rendered inaccurate. In this context, we also note that FPVG's behav- ior has not been explicitly explored for cases with ambiguous relevance annotations.
Summary. Our analysis shows that VQA models have clear tendency make mistakes in OOD for questions that are not not faithfiully grounded. This tendency consistently observed across various model types and model instances. Our findings support the idea that weak visual grounding is detri- mental to accuracy in OOD scenarios in particular, where the model is unable to fall back on learned Q/A priors to find the correct answer (as it can do in ID testing). Furthermore, we note that VisFIS, which boasts considerable improvements in FPVG and strong improvements in accuracy over basic UpDn, unable to overcome these problematic ten- dencies. This suggests that VG-boosting methods alone might not be enough to overcome a model's fixation on language-basee based priors, which is exacer- bating the performance gap between ID/OOD.
Secondly, FPVG creates its visual input modula- tions by matching annotated objects with objects detected by an object detector. Different object detectors can produce bounding boxes of varying accuracy and quantity depending on their settings. When using new object detector as a source for visual features, it might be necessary to re-ajjust parameters used for identifying relevam/irrelevant objects (see App. A for settings used in this work). When doing so, the integrity of FPVG can only be retained when making sure that there are no overlapping objects among relevant & irrelevant sets.
5 Conclusion
We introduced Faithful & Plausible Visual Ground- ing (FPVG), a metric that facilitates and stream- lines the analysis of VG in VQA systems. Using FPVG, we investigated VQA systems of various architectural designs and found that many models struggle to reach the level of faithful & plausible VG that systems based on symbolic inference can provide. Finally, we have shown that FPVG can be a valuable tool in analyzing VQA system be- havior, as exemplified by investigations of the VG- OOD relationship. Here, we found that VG plays
Thirdly, comparing VQA models with FPVG across visual features produced by different object

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detectors might be problematic/inaccurate in itself, as 1) different numbers of objects are selected for relevant & irrelevant sets, and 2) different Q/A sam- ples might be evaluated (e.g., due to missing de- tections of any relevant objects). If possible, when using a new object detector, we recommend includ- ing FPVG evaluations for some reference model(s) (c.g.. UpDn) as an additional baseline to enable improved assessment of a model's FPVG mea- surements that are trained with a different object detector's visual features.
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A Determining relevant objects.
Theoretical considerations. One motivation for considering an answer change when testing with irrelevant parts given in $3.2. namely that it un- both relevant and irrelevant objects. e.g. no question-relevant objects were detected, the ques- tion is excluded. Hence, different subsets of the test (=balanced val set) are evaluated depending the used object detector. Table 5 lists some statis- ties related to this for each of the object detectors used in our evaluations. The set of relevant objects is determined by ToU > 0.5 between detected annotated bbox. The set of irrelevant objects ex- cludes all detected bboxes that cover > 25% of any annotated relevant object to avoid any significant inclusion of relevant image content.
FPVG can only be meaningfully evaluated with questions for which the used object detector found
covers cases where the model simply indifferent
to visual input entirely. This indifference to visual input is a major (language) bias problem in VQA. Hence, it is important to have mechanism that can identify these cases.
Empirical investigation. We investigate results produced by the modified FPVG version. We mod- ify FPVG to only consider answer changes when testing with relevant objects (i.e., ignoring the third test involving irrelevant objects and therefore re- moving the condition for Airret from FPVG's for- mulation). Results of this modified FPVG metric (mod F FPVG) for ID/OOD tests over five runs (same tests that were discussed in $4.4) are shown in Table
#ohj
#obj avg rel imrl
Obj. Detector
#Q/A
max
all
Detectron2 (Wu et al., 2019)
114k
100
91 5 62
VinVL (Zhang al., 2021)
110k
31
Table Object detector bbox statistics for FPVG evalu- ation.
Results of mod_FPVC FPVG appear to be less reasonable than the original FPVG. E.g., VLR, which achieved by far the best FPVG+ (and OOD accuracy) with the original FPVG, is now ranked behind VisFIS and close to UpDn. MMN, which had the best OOD performance among classification-base models (and was ranked third in F FPVG, 1) is now ranked last by a large mar- gin. Based on the known architectural properties of these models (e.g.. using VG-focused mecha- nisms in MMN and VLR), such rankings would be surprising.
Discussion.
B Metric formulation addendum
Mathematical formulation of each of FPVG's four subcategories is as follows (for description of the variables used in the formulae, see $3.1):
E
(9)
(1 o,)))
(10)
We also investigate the c2i ratios for mod_FPVG
the same scenario (see Ta-
FPVGT= E; (1- 2.))
(11) ble 7 and Fig. 6). Here, we observe opposite trends to the ones shown in the main paper for original FPVG. In particular, these new results suggest that well grou-ngounded questions (as per mod_ [ind_FPV FPVG G) are much more prone producing wrong answers in OOD vs. ID than badly- questions (as il- lustrated by larger degradations for mod_ PVG.. than mod F FPVG_ in Fig. 6, left). This does not align with any reasonable expectation for a model's OOD behavior and we think it again points to problems with the modified metric.
FPVG== (1-FFVV) Eq 9,1)) (12)
Eq. 9-12 to 1. See Fig. 2 for illustration, where maae-to-equatton correspondence is given by (q) 9, (b)-Eq. 10, (c)-Eq. 11, (d)-Fq. 12.
C Metric investigations - modified FPVG
During the paper review of this work, investiga- tions were requested to show the value of FPVG's third test case (which involves irrelevant objects and is run acquire Airret) by exploring FPVG's behavior when Agrett is omitted. We include our findings here. Note, that this section assumes that the reader has read the main paper.
These empirical results on top of the mentioned theoretical considerations emphasize the value of including tests with irrelevant objects in FPVG.
Conclusion.

---

Accuracy
FPVG
mod FPVG,
Model
ID
0OD
ID
OOD
ID
OOD
UpDn
51.41.58
30.83:1..6
17.5+.87
19.332.73
77.56+1143
76.33:11.23
HINT
51.28.139
31.344.55
18.06=11223
19.59#.68
74.81=1.62
75.37+22.00
VisFIS
53.28.1.44
33.42:1:103
25.1
25.18
82.25:10.40
80.15:10.11
MAC
52.1:.46
31.31
15.4..51
16.72#.22
73.93+2.08
73.27+22.56
MMN
4..28
3..4
18.74+.32
17.88
61.99++0.2
58.66+1.115
VLR
55.64
56.38
37.56
38.51
79.48
79.03
Table 6:
Accuracy (i.c., Accau) and mod_ FPVG+ for models evaluated with GQA-101k over five differently seeded training runs.
mod_ FPVG_
mod PPVG
90
90
mod_FVVC
mod_FPVS
80
UpDa
1662-06
.911.08
331.00
.22-.01
70
70
HINT
1468-05
.61+-03
38+-03
.24-00
1.75-05
50
50
MAC
1.74_07
.6.1.03
42
.991.02
40
40
MMN
2.29-06
98++06
46+01
.33-002
30
30
VLR
2.01
2,10
.25
.24
20
10
10
000
Uptm
HINT
ViRs5
MAC
MMN
HA
UpDn
NINT
ViFSs
MAC
MMN
VLR
Table 7: Correct to incorrect (c2i) an- swer ratios for questions categorized as mod_
Figure 6: Performance drops when comparing ID to OOD (questions in LLod_FPVGG/0 [+. left), and when comparing mod_ FPVG, to mod__PVC (questions in ID/OOD, right). Data set: GQA-10Ik.
FPV Foo__FPGGrp 101k.
Data set: GQA-
D Feature importance
include replacing omitted objects with cer- tain other values (e.g., constants (Ying et al., 2022), objects from other images (Yuan et al., Gupta et al., 2022)).
D.1 Methods for measuring feature importance
2021;
In 3.3, we consider three methods to measure fea- ture importance, one representative from each of the three categories commonly used in VQA, de- scribed in more detail in the following:
D.2 Feature importance ranking scores
Scores Table were calculated as follows: A question's "relevant" score measures how many of N annotated relevant objects in set relN are among the top. relevant objects (as deter- mined and ranked by the used metric), It is cal- culated as topNDrelN where higher value is desir- relN able for FPVG,). A question's "irrelevant" score measures how many of M innotation-determinng irrelevant objects in set irrelM are among the MopM metric-determined relevant objects. cal- topMrirrell with a lower value being culated as irrelM desirable for FPVGL.
1. Mcasuring attention (direct): Attention over input objects gives a sense of importance the model assigns to each object (used, e.g., in Li et al. (2019); Urooj et al. (2021); Hudson and Manning (2019)).
2. Measuring gradients (direct): Gradient-bast based methods like GradCAM are close to the model's inner workings as they involve es- timating a direct link between the importance of the input features and model's 's output de-
cision (used, e.g., in Selvaraju et al. (2019); Wu and Mooney (2019)).
E Model Training
3. Feature manipulation (indirect): Usually by omission of input entities (i.c., vectors rep- resenting objects). The manipulated image representation can be zero-padded main- tain the model's size expectations, as is com- monly done for variable length inputs in se- quence modeling. Other variants used VQA
In this section we include details for training pro- cedures of models used in this work's 's evaluations. Generally, we GQA's balanced train set to train all models and the balanced val for evaluations. small dev set (either small, randomly excluded partition the train set (20k questions), or sepa- rately provided in case experiments on GQA-

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101k (Ying al., 2022)) is used for model selec- tion.
balanced train set (Im questions). We use early stopping of epoch and select the model by best accuracy on the dev set (using Oracle programs in bootstrapping mode and predicted programs in fine-tuning mode). The program parser was not retrained.
E,0.1 Visual Features
The object detector used in this work is Faster R-CNN (Ren et al., 2015) model with ResNet101 (He et al., 2016) backbone and an FPN (Lin et al., 2017) for region proposals. We trained this model using Facebook's 's Detectron2 framework (Wu et al., 2019). The ResNet101 backbone model was pre- trained on ImageNet (Deng et al., 2009).
E.0.3 DFOL
DFOL (Amizadeh et al., 2020) uses vanilla seq2seq program parser, but neither code nor gener- ated output for this is provided in the official code base. Thus, evaluations are run with ground-truth programs from GQA. DFOL is trained on a loss based on answer performance to learn weights in its visual encoding layers that produce an image representation similar to the one used by VLR (Re- ich et al.. 2022), given high-dimensions visual features as input.
The object detector was trained for GQA's 1702 object classes using 75k training images (images in GQA's train partition). Training lasted for Im iterations with mini-batch of images, using a multi-step learning rate starting at 0.005, reducing it by a factor of 10 at 700k and again at 900k iter- ations. No other parameters were changed in the official Detectron2 training recipe for this model architecture. Training took about 7 days on an RTX 2080 Ti.
Training is done based on the official instructions for a complex 5-step curriculum training procedure. We train the first 4 curriculum steps with the entire 14 million questions in GQA's "all" training data partition, as specified in the instructions. As this is extremely resource intensive, we train for one epoch in each step. Finally, we run the 5th step with the "balanced" train data only (Im questions) until training finishes by early stopping of I epoch.
We extract 1024-dim object-based visual fea- tures from layer in the object classification head this model which acts as input to the final fully- connected softmax-activated output layer. Up to 100 objects per image are selected as follows: per-class NMS is applied at 0.7 ToU for objects that have any softmax object class probability of 0.05.
E.0.4 MAC
Note that with exception of GQA-101K s reparti- tioned test sets (which mix questions from balanced train and val sets), no images used in testing were used in training.
MAC (Hudson and Manning, 2018) is monolithic VQA model based on a recurrent NN architecture which allows specification of the number of infer- ence steps to take over the knowledge base. We follow the official training procedure guidelines given in the released code base and use 4-step in- ference. We train the model on GQA's balanced train set and use early stopping of epoch based accuracy on dev set to select the best model.
Most models are trained with Detectron2-based visual features (1024-dim object-based visual fea- tures for 100 objects/image max) input. For OS- CAR+, we use the officially released pre-trained base model which uses VIn visual features (Zhang et al., 2021).
E.0.5 UpDn, HINT, VisFIS
E.0.2 MMN
UpDn (Anderson al., 2018) is classic, straight- forward attention-based model with single at- tention step before merging vision and language modalities. We use the implementation shared by (Ying et al., 2022). Following the scripts there, we train UpDn for 50 epochs and select the best model based on accuracy on dev set.
MMN (Chen al., 2021) consists of two main modules that are trained separately: A program parser and the actual inference model, which takes the predicted program from the parser as input. We mostly follow the settings in the official code-base but detail some aspects of our customization here.
For the inference model, we run up epochs of bootstrapping (using GQA's "all" train set (14m questions)) with Oracle programs and another up to 12 epochs of fine-tuning with programs (from the official release), using GQA's
HINT (Selvaraju al., 2019) and VisFIS (Ying et al., 2022) are two VG-improvement methods. VisFIS trained according to the released scripts. HINT is trained according to Shrestha et al. (2020) (using the VisFIS codebase), i.e. we continue train-

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ing the baseline UpDn model with HINT (using GQA annotations determine importance scores) for 12 more epochs and select the best resulting model (aceuracy on dev set).
E.0.6 VLR
VLR (Reich et al.. 2022) is a modular, symbolic method that requires a full scene graph as visual representation. Similar to DFOL and MMN, it makes use of (trained) program parser. The ac- tual inference module does not require training. Training of the program parser and generation of the scene graph was done according to the descrip- tion in Reich et al. (2022). The scene graph was generated using the same Detectron2 model that produced the visual features for the other models in this work.
E.0.7 MCAN
MCAN (Yu et al., 2019b) is Transformer-based model that uses co-attention layers and form of multi-hop reasoning to hone in on attended vision and language information. We use the model imple- mentation by Yu et al. (2019a) to train the "small" model (6 layers).
E.0.8 OSCAR+
OSCAR (Li et al., 2020) is SOTA Transformer- based model that leverages pre-training various V+L tasks and data sets. The subsequent release of new and elaborately trained visual features, known as VInVI. (Zhang et al., 2021), further elevated its performance. We use this stronger version of OSCAR, called OSCAR+, in our evaluations. For training, we leverage the officially released pre- trained model and the VInVL features. Fine-tuning is done GQA's balanced val set according to instructions accompanying the official release.
Note that we included results of UpDn (named "UpDn#", last line Table 2) trained with these stronger VL features, accordance with our recommendation in the Limitation section ($6) for new visual features.


Reviews:
[{"review_id": "j0x8G6Esdc", "reviewer": "Reviewer_fV5W", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer fV5W (hereafter “R1”) for their feedback.\n\nR1 recommends a more detailed description of Figure 2. We’ll gladly add this to the final version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper is well written. The problem and solution is well articulated and backed up by solid results. The metric FPVG is simple and intuitive. The research community can widely adopt this metric to measure VG in VQA tasks.", "reasons_to_reject": "None", "questions_to_authors": "", "paper_topic_and_main_contributions": "The authors propose a new Visual Grounding (VG) metric for VQA (Visual Question Answering) that captures (i) if a model identifies question relevant objects in the scene and (ii) actually relies on the information contained in the relevant objects when producing the answer. The authors name their metric Faithful and Plausible Visual Grounding (FPVG) and evaluate it on several VQA architectures. They also provide the code for the calculations.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Figure 2 was difficult to follow. I would recommend making it clearer."}, "extra_text": {}}, {"review_id": "QXmzZPwFqC", "reviewer": "Reviewer_9Aq7", "summary": "", "questions": "- Does FPVG+ col in Table 3 mean the accuracy for the FVVG cases where the conditions (no change for relevant + change for irrelevant) are met? \n- Why does Table 3 only have FPVG+ cases and not the - ones as well? \n- See more questions in the weakness section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer 9Aq7 (hereafter “R2”) for their feedback. We particularly value R2’s clearly stated requirements for a rating improvement and hope we satisfied these requirements below.\n\n**Reject1:** \nOne motivation for considering an answer change when testing with irrelevant parts is given in L271ff, namely that it uncovers cases where the model is simply indifferent to visual input entirely. This indifference to visual input is a major (language) bias problem in VQA and hence it’s important to have a mechanism that can identify these cases. \n\nNevertheless, for R2’s consideration, we modified FPVG to only consider answer changes when testing with relevant objects as per R2’s request. Average metric results for the ID/OOD tests are as follows:\n\n| Model |  ID | OOD |\n| --------| -------| ------|\n|UpDn | 0.77 | 0.77 |\n|HINT| 0.75 | 0.75|\n|VisFIS| 0.82 | 0.80|\n|MAC| 0.73 | 0.73|\n|MMN| 0.62 | 0.58|\n|VLR| 0.79 | 0.79|\n\nResults of R2’s proposed metric appear to be less reasonable than FPVG. E.g. VLR, which achieves by far the best FPVG and OOD performance is now ranked behind VisFIS and close to UpDn. MMN, which had the best OOD performance among classification-based models (and was ranked third in FPVG) is now ranked last by a large margin. Based on the architectural properties of these models, such rankings would be very surprising.\n\nWe also investigated the c2i ratios for R2’s metric in the OOD scenario (not posted here, but can be shared if required). Here, we observe opposite trends to the ones shown in the paper. In particular, these new results suggest that well-grounded questions (as per R2’s proposed metric) are much more prone to producing wrong answers in OOD vs ID than badly-grounded questions. This doesn’t align with any reasonable expectation for a model’s OOD behavior and we think it again points to problems with R2’s proposed metric.\n\nThese additional results on top of the motivation / explanation in the paper (L271ff) emphasize the necessity of including tests with irrelevant objects in FPVG. We hope this satisfies R2's concerns.\n\n\n**Reject2:** \n- FPVG’s 4 cases: The subdivision of FPVG by answer correctness facilitates in-depth analysis. E.g., they provide the basis for the analysis in Sec. 4.4 (c2i ratios). We find these additional categories helpful and informative in our research (also beyond this paper) and hence found it important to explicitly point them out.\n\n- Table 2 trends in 8th/10th column: Since there is no intuitively sensible ranking for the mentioned categories under the FPVG motivation, we made a conscious decision not to assign any ranking directive here. That does not however mean that these two categories are irrelevant, as they can be informative for different types of analyses (as shown in Sec. 4.4). We'll explicitly point this out in the final revision.\n\n\n**Reject3:** \nTable 3 shows both Accuracy and FPVG+, both over the same set of questions. The two FPVG+ columns in Table 3 list actual FPVG+ values and not accuracy. Looking at Table 3, we find evidence that FPVG+ does not linearly predict OOD accuracy. We discuss this and identify reasons for it in Sec 4.4, L558ff. We'd like to point out that some evidence was presented in Ying et al. (2022) to suggest that VG, or RRR metrics in general, might not be an ideal predictor for OOD accuracy. Still, our paper shows: while using FPVG+ as a straight-up predictor for accuracy might be problematic, FPVG-based analysis can be used to explain a model’s obstacles in OOD settings, as well as the role VG plays in VQA models for achieving OOD performances on par with ID settings.\n\n\n\n**Reject4 (without bullet point):**\nWe’ll look into adding additional highlighting for the take-away points, as requested by R2. Other points mentioned in this paragraph were addressed above.\n\n**Question1:** \nSee Reject3 above. We'll add clarification to Table 3's caption in the revision.\n\n**Question2:** \nFPVG- is not listed because it can be calculated based on the listed FPVG+ values (see Eq.6: FPVG- = 1 - FPVG+).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This direction of measuring how much the object grounding is actually being used in the model predictions is important in evaluating models and making sure they don’t rely on biases. \nThe fact that a model answer should not change when cropped to relevant region of the image is a clean and simple way to make sure the model is consistent and grounded.  \nThey show that FPVG and accuracy do not correlate with each other and hence, it provides additional information. \nThey show that the correct-to-incorrect ratio drops significantly for out-of-domain questions on the cases that their metric deems as bad cases vs good cases, showing that their metric is suggestive of OOD performance.", "reasons_to_reject": "- I agreed with the authors on the their first premise - when only the relevant part of the image is cropped, the answer shouldn’t change. \nHowever, I do not agree with the second part. When cropped to the irrelevant parts, I do not agree that the answer *has* to change. It may or may not change. This makes the metric overly harsh and might penalize a model unnecessarily. It might be nice to see if the second part of the metric is required for it to correlate well with OOD performance etc. \n\n- The writing and flow could be improved. I am not clear why the FPVG has to be decomposed into 4 cases. Not sure what the differentiation of VQA answer correct cases and incorrect cases is giving us. It seems like all the analysis and results explained are based on the + and -. It is unclear in Table 2 what trend to look for in the 8th and 10th columns. \n\n- The result presentation can be simplified. It is hard to know which number to compare with which. It’s unclear what Table 3 is actually showing, Is it showing the FPVG+ metric number or the accuracy on the cases where the FPVG conditions are met? Which numbers are we actually comparing here?  Is it that the OOD for FPVG column correlates with the OOD accuracy? Explicitly pointing these out in the result section would help. \n\nThe results look promising. If the authors can 1) justify that having the requirement of answer changing when irrelevant is required or at least not hurtful (even if overly harsh) to ranking better models higher, and 2) improve and simplify the presentation of results (make key take away points as headings and explicitly point to numbers using row/col numbers if required in the Tables we should be looking to support the claim), I will be willing to increase my rating.  \n\n**[EDIT] I have upgraded my rating due to the inclusion of the experiments on how the metric correlates with OOD performance if the requirement for the answer to change for irrelevant input is removed. This is interesting, and I hope to see some discussion and intuition behind why this is the case.**", "questions_to_authors": "- Does FPVG+ col in Table 3 mean the accuracy for the FVVG cases where the conditions (no change for relevant + change for irrelevant) are met? \n- Why does Table 3 only have FPVG+ cases and not the - ones as well? \n- See more questions in the weakness section.", "paper_topic_and_main_contributions": "Authors propose an evaluation paradigm of measuring faithful visual grounding in VQA. They propose that if an image is cropped to the relevant regions, the answer of a VQA system should remain unchanged compared to the full image. However, if the image is cropped to irrelevant regions, it should waver from the original answer. They make a metric based on this intuition and show that it is suggestive of OOD performance of VQA models.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "See some points in the weaknesses section."}, "extra_text": {}}, {"review_id": "jyZbY7jNUZ", "reviewer": "Reviewer_bzSZ", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer bzSZ (hereafter “R3”) for their feedback. \n\n**Reject1:** \n\nR3 expresses concerns about the novelty of FPVG as a VG metric and points to Shrestha et al. (2020) as supporting reference. \n\nWe strongly disagree with R3’s assessment in this point. For context: the metric used in Shrestha et al. (2020) relies on GradCAM measurements for calculating a ranking correlation between a model’s object rankings and the rankings based on object relevance annotations. GradCAM has been found to be among the least faithful measurement methods in the context of VQA’s UpDn model in Ying et al. (2022). FPVG on the other hand is shown in the paper to exhibit strong evidence of faithfulness (Sec 3.3). Moreover, the two methods are based on fundamentally different approaches to determine VG, with FPVG relying on feature manipulation and Shrestha et al. (2020) relying on gradient measurements. Essentially, the only thing the two methods have in common is their goal of measuring VG w.r.t. relevant image regions. Given these fundamental differences, it appears that R3’s expectation of novelty is that two methods mustn’t share the same goal* -- which is an extraordinarily restrictive opinion to maintain.\n\n*Indeed, R3 clearly states this opinion in their reasoning to reject the paper, namely “several metrics for assessing visual grounding capabilities in VQA tasks already exist”.\n\n\n**Reject2:**\n\nR3 states that the paper lacks detailed comparisons of faithfulness in existing metrics.\n\nR3 argues a moot point here and we’ll explain why. We’d like to clarify, that the paper’s goal is to introduce a new VG metric and show that it 1) measures VG in VQA in a “faithful” and “plausible” manner, and 2) is reliable and versatile to use in practice. These goals do not require the paper to provide a large-scale empirical study that ranks VG methods by faithfulness in a “detailed comparison”. Instead, the paper can (and does) accomplish the two listed goals as follows:\n\n1. The paper addresses (1) by validating FPVG’s faithfulness property with three fundamentally different methods, two of which (LOO, Attention) have been found to provide the most faithful feature relevance determination in this particular experimental setting (see Ying et al. (2022)). The used methods each represent one of the three major categories of existing VG metrics, as mentioned in L313ff (incl. Shrestha et al. (2020)’s GradCAM based metric). \n\n2. The paper addresses (2) by providing extensive evaluations across a large number of different VQA model architectures in both ID and OOD scenarios, hence demonstrating FPVG’s high utility in practical evaluations that other VG metrics might lack (see L75ff, L134ff, L148ff for problems when applying various other metrics). Experimental observations of FPVG consistently align with various theoretical assumptions made about VG in VQA context, such as VG’s role in OOD scenarios (Sec 4.4) and VG strength of certain model architectures like VLR (Table 2 and 3). The consistency of these results speak for FPVG’s reliability. The demonstrated applicability of FPVG to a wide variety of model architectures highlights its versatility.\n\nIn summary, the paper provides adequate and sufficient support for its claims.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- A metric to quantify a model’s faithful reliance on plausibly relevant image regions is proposed and can add value to the VQA research community.", "reasons_to_reject": "- Lack of novelty. Several metrics for assessing visual grounding capabilities in VQA tasks already exist. The research questions explored in this paper may be limited in their degree of innovation compared to prior work. For example, Shrestha et al. (2020) also aimed to quantify VG capabilities based on relevant image regions.\n- Lack of comparison with existing metrics, especially from the perspective of faithfulness and plausibility—the core characteristics of the proposed metric. While Table 1 provides some experimental results compared with existing metrics, a detailed comparison that focuses on these key characteristics would strengthen the paper's contribution and better highlight the advantages of the proposed approach.\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper introduces a new metric for evaluating the visual grounding abilities of VQA models, specifically focusing on the model's faithful and plausible reliance on relevant image regions. The paper provides experiments on multiple models with the proposed metric, which shows the effectiveness of the proposed metric.", "excitement": 2, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a new Visual Grounding (VG) metric for VQA aiming to balance both faithfulness and plausibility, i.e. (i) if a model identifies question relevant objects in the scene and (ii) actually relies on the information contained in the relevant objects when producing the answer.

The majority of reviewers felt that the paper is well written with conclusions backed up by experiments. The new metrics give new insights that are not captured by existing ones, and can inspire the design and evaluation of new methods.

There were some concerns over writing and structuring of the paper, as well as concerns regarding some design decisions in the metric by reviewer 9Aq7, which the authors adequately addressed during discussion. Reviewer bzSZ raised several issues as well, in particular that the metric overlaps slightly with other investigations regarding metrics for grounding, which deserves additional comparisons. I believe the authors gave satisfactory responses regarding the differences in their method, but I would still strongly suggest adding a discussion wrt these other metrics. How can researchers decide which of these metrics to use or optimize their models for? Can we study the similarities and differences between these metrics systematically?
Decision: Accept-Findings