Grounded Video Caption Generation

## Abstract

Abstract We propose a new task, dataset and model for grounded video caption generation.
This task unifies captioning and object grounding in video, where the objects in the caption are grounded in the video via temporally consistent bounding boxes. We introduce the following contributions.
First, we present a task definition and a manually annotated test dataset for this task, referred to as GROunded Video Caption Generation (GROC).
Second, we introduce a large-scale automatic annotation method leveraging an existing model for grounded still image captioning together with an LLM for summarising frame-level captions into temporally consistent captions in video.
Furthermore, we prompt the LLM to track by language – classifying noun phrases from the frame-level captions into noun phrases of the video-level generated caption. We apply this approach to videos from the HowTo100M dataset, which results in a new large-scale training dataset, called HowToGround, with automatically annotated captions and spatio-temporally consistent bounding boxes with coherent natural language labels.
Third, we introduce a new grounded video caption generation model, called VideoGround, and train
the model on the new automatically annotated HowToGround dataset.
Finally, results of our VideoGround model set the state of the art for the new task of grounded video caption generation. We perform extensive ablations and demonstrate the importance of key technical contributions of our model.

## 1 Introduction

In recent years, we have witnessed tremendous progress in multimodal video understanding thanks to Large Language Models and advanced designs that exploit the synergy of video and language. Current efforts have focused on a variety of tasks that require reasoning about (possibly long) videos together with a certain level of comprehension of natural language. Examples include natural language video captioning (Chen & Jiang, ; Fujita et al., ; Huang et al., ; Mun et al., ; Tang et al., ; Wang et al., ; ; Zhou et al., ),
temporal alignment of video with language (Han et al., ; Ko et al., ; Sigurdsson et al., ; Yang et al., ),
finding relevant moments in videos given a language query (Gao et al., ; Hendricks et al., ; Lei et al., ; Zhang et al., ; ; Zhu et al., ),
or video question answering (Engin et al., ; Kim et al., ; Le et al., ; Lei et al., ; Li et al., ; Park et al., ; Yang et al., ; Yu et al., ; Yang et al., ; Zeng et al., ).
Others have looked at producing bounding boxes of events in video given natural language descriptions (Tang et al., ; Zhang et al., ; Huang et al., ) or given natural language questions (Lei et al., ).
Overall, these efforts have focused on producing video-level or moment-level outputs, such as (temporally localized) captions, or producing single event-level bounding boxes in video.

At the same time, producing natural language video descriptions where the described objects are spatio-temporally grounded with bounding boxes in videos has received much less attention. Progress on this problem is, however, important as spatio-temporal grounding of natural language on a large scale is an important step to advance areas such as human-robot interaction and embodied perception (Li et al., ; McCarthy et al., ; Patel et al., ; Sermanet et al., ; Zorina et al., ).
Key factors limiting progress on this problem are the lack of annotated testing data, dedicated grounded video caption generation models and appropriate large-scale training datasets, which are costly to manually annotate.

In this work, we aim to narrow this gap by the following four contributions.
First, we introduce the grounded video caption generation task together with a manually annotated test dataset of 1000 videos, which we name GROunded Video Caption Generation (GROC). This allows to measure progress on this challenging problem.
Second, to address the issue of limited training data, we introduce a large-scale automatic annotation method leveraging an existing model for grounded still image captioning together with an LLM to summarize frame-level captions into video-level captions. The LLM is also tasked to *track by language*, associating frame-level phrases that correspond to objects with video-level phrases, resulting in object tubes with a consistent label. We apply this approach to videos from the HowTo100M dataset, which results in a new large-scale training dataset, called HowToGround, with
automatically annotated captions and spatio-temporally consistent bounding boxes with
coherent natural language labels.
Third, we introduce a new grounded video caption generation model, called VideoGround. The key technical contributions of this model include: (i) spatio-temporal adapters, which enable efficient modeling of spatio-temporal information in video; (ii) bounding box decoder and that outputs temporally coherent bounding boxes in video and (iii) temporal objectness head that explicitly models objects that temporally leave the frame or are occluded. We train the VideoGround model on the automatically annotated HowToGround dataset.
Fourth, we perform extensive
ablations and demonstrate the importance of key technical contributions of our model.
Results of our VideoGround model set the state of the art for the new task of grounded video caption generation.

## 2 Related Work

Image-based grounded data generation. Recently, there has been an increased interest in developing large multi-modal models capable of grounding text to images as well as comprehending referring expressions (Zhang et al., ; Peng et al., ; Zhao et al., ; Chen et al., ; Ma et al., ). The scale of these models dictates that they should be trained on large-scale datasets. As obtaining grounding datasets manually is extremely laborious, particularly at this scale, methods typically resort to pretrained models to harness these data. Zhang et al. introduced the grounded visual chat task and generated a dataset for the task using GPT-4 (OpenAI et al., ) along with visual instruction tuning data that are paired with ground truth bounding boxes. Rasheed et al. () introduced a complex automated annotation pipeline for grounded conversation generation consisting of multiple stages and resorting to a variety of pretrained models to obtain a large scale pseudolabelled dataset. Peng et al. () proposed to use
Part-of-Speech tagging for extracting noun chunks from the captions and fed them to a pretrained grounding module for pairing them with bounding boxes.  Chen et al. () proposed a model for referential dialogue. To train the model, they combined existing grounding and referring expression comprehension data along with QA pairs associated with bounding boxes generated with GPT-4. We build on this line of work but focus on the video domain.

Datasets for spatio-temporal grounding in video.
The existing datasets (Tang et al., ; Zhang et al., ; Huang et al., ; Tan et al., ; Chen et al., ; Zhou et al., ) for spatio-temporal video grounding are relatively small scale as they rely on manual annotation, which is tedious and time consuming. Typically, the existing datasets also focus on a single grounding bounding box (Tang et al., ; Zhang et al., ; Tan et al., ; Chen et al., ), which can be a limiting factor in instructional videos where multiple objects are often manipulated. Finally, the focus is often on the task of predicting bounding boxes given a natural language description (Huang et al., ) or natural language questions in a video question answering task (Lei et al., ). In contrast, we create an automatic annotation method for spatio-temporally grounded captions, which allows us to create a large-scale dataset. In addition, we focus on the task of grounded video caption generation, which requires generating both the natural language description as well as multiple bounding boxes grounding individual described objects in the video. The dataset of Zhou et al. () is closest to ours in that it provides multiple objects per frame along with annotated noun phrases
in the caption. Nevertheless, the authors annotate a single frame per video
segment while our automatically annotated HowToGround training set has an average of 43.5 annotated frames per video segment. For a more detailed comparison with other datasets as well as statistics of our proposed datasets, please see Section A in the Appendix.

Methods for spatio-temporal grounding in video. Spatio-temporal video grounding (Tang et al., ; Zhang et al., ; Yang et al., ; Tan et al., ; Lin et al., ; Chen et al., ; Su et al., ; Zhang et al., ; Jin et al., ; Gu et al., ; Wasim et al., ) is the task where given a natural language description and a video, a model should predict a single spatio-temporal tube enclosing the full event described in the text. Zhang et al. ()
introduce a spatio-temporal graph encoder for multi-step reasoning using as input features of the detected objects. Many works have since relied on object detection features for spatio-temporal grounding. Tang et al. () extract object proposals and feed both language tokens and detection features into a multi-modal transformer. Yang et al. () adapt the MDETR architecture to spatio-temporal video grounding. Tan et al. (); Chen et al. () propose a
multi-modal contrastive learning frame-work for spatio-temporal grounding by training on videos from HowTo100M. Lin et al. () introduce a two-stream architecture for modelling appearance and motion.
Gu et al. () introduced a Context-Guided Decoder where queries are enhanced with rich visual cues generated from an Instance Context Generation module. All these works are limited in that they do not have a module for text generation as in the spatio-temporal grounding task the text is given, nor can they generate multiple spatio-temporal tubes for multiple objects in the video. Our task formulation, automatic annotation method and the proposed model address these limitations.

## 3 GROunded Video Caption Generation: task, datasets and metrics

Task definition. We introduce the GROunded Video Caption Generation (GROC) task for video understanding. Similar to Grounded Caption Generation for images, the task is to both generate a caption for the video and also predict bounding boxes across frames for noun phrases from the sentence. Differently than the image-based task, where bounding boxes are always predicted for the tagged/predicted noun phrases from the sentence, in the GROC task, as objects might disappear in some frames, there should not be bounding boxes predictions for the disappearing objects in those frames. Therefore, the task has the extra difficulty that a mechanism is needed to decide at each frame of the video whether to predict a bounding box for a given phrase from the sentence or not. Additionally, the temporal dimension of videos adds extra challenges as bounding boxes need to be spatio-temporally smooth across frames. To summarise, given a video the GROC task entails: i) predicting a caption describing the video, ii) tagging noun phrases in the caption that correspond to objects that will be grounded, iii) predict bounding boxes across frames for the tagged objects from the caption. As already discussed, as objects can disappear and reappear, there might be discontinuities in the bounding box predictions; in other words, there can be more than one spatio-temporal tubes for each object with gaps in between that correspond to objects disappearing at certain frames due to occlusions.

GROC: Manually annotated evaluation dataset for grounded video caption generation. We manually annotate the GROC evaluation dataset using videos from the HowTo100M dataset (Miech et al., ).
We provide manual annotations for 1100 video clips.
We construct the validation set by randomly sampling 100 manually annotated clips. We form the test set using the remaining 1000 manually annotated clips. The annotation procedure consists of 2 steps: i) video selection where ‘interesting’ videos are selected from a large pool of videos randomly drawn from HowTo100M, and ii) video annotation where videos are annotated with
a caption, bounding boxes and noun phrases from the caption associated with the bounding boxes.
The video annotation itself consists of 3 steps. The first step entails watching the video and providing a description of what is happening in the video and the objects that are being used. Note, that we are interested in the active objects, i.e. objects that humans interact with, rather than densely describing all objects in the scene. In the second step, we annotate with bounding boxes all visible instances of humans/objects from the caption that has been provided in the previous step. The bounding boxes are applied to all frames where the humans/objects are visible. Finally, we annotate each bounding box with a short phrase or word which should match exactly a short phrase/word from the caption. For more information about the annotation procedure including the exact annotation guidelines and the mechanisms for assuring the consistency and the overall quality of the annotations, please see Section F in the Appendix.

Evaluation metrics. We build on the metrics for grounding captions in still images Rasheed et al. () and adapt them to our task. These include METEOR (Banerjee & Lavie, ) and CIDEr (Vedantam et al., ) for the quality of the captions, AP50 for the accuracy of grounding bounding boxes to phrases, mean IoU across videos to measure the bounding box detection quality, and the recall Rasheed et al. () that combines IoU and the similarity of embeddings of the predicted phrases that correspond to bounding boxes. The aim of the recall metric is to assess the rate of positive predictions from the model, where a prediction is considered positive if both the IoU and the phrase similarity is above a certain threshold. We propose two different settings of AP50, mIoU and recall for the GROC task: i) frame-level evaluation where the metrics are calculated across all frames of all videos – same as for images and ii) video-level evaluation where the metrics are calculated per video and averaged across videos.

## 4 Automatic annotation method and HowToGround dataset

Figure: Figure 2: A method for automatic annotation of spatio-temporally grounded captions. In the first stage (left), we apply a still-image grounded caption generation model on individual video frames producing temporally inconsistent outputs. In the second stage (middle), the captions from individual frames are aggregated using an LLM into a single video-level caption describing the most salient actions/objects in the video. Third (right), individual frame-level phrases and bounding boxes are associated over time into a temporally consistent labelling of object bounding boxes over the video.
Refer to caption: x2.png

### 4.1 Automatic annotation method

We describe our method for generating a pseudolabelled dataset for grounded video caption generation. Given an unlabelled dataset of videos depicting humans interacting with objects or other humans, the goal of the method is to generate both video-level captions describing what is happening in the video and bounding boxes grounded to the phrases from the caption that describe the main objects in the video. We leverage foundation LMMs and LLMs as they have been pretrained on large-scale datasets and are rich sources of information. Our method consists of three steps, as shown in Figure 2: i) frame-wise grounded caption generation, ii) video-level caption aggregation and iii) tracking by language. We describe these steps next.

Stage 1: Frame-wise grounded caption generation. We start by applying grounded caption generation, which is the task of generating a text description for the input data along with predicting bounding boxes for the parts of the sentence (phrases) that describe the objects present in the images/videos. Since there are no available video models for this task, we can utilise image-based models and run them in a frame-by-frame basis. We adopt GLaMM (Rasheed et al., ), as it has shown very good performance in image-based grounded video captioning. GLaMM generates grounded segmentation masks which we convert to bounding boxes. An example of the outputs of this step can be seen in Figure 2, left. It can be seen that GLaMM generates temporally inconsistent captions across frames, since it does not have access to video-level information. We address this with the next two steps of our proposed method.

Stage 2: Video-level caption aggregation. Given the frame-level captions from the previous stage, we wish to obtain a video-level caption describing the most salient actions/objects in the video. We also wish to segment phrases from the caption that correspond to the objects appearing in the video, given the frame-level phrases. These will constitute the labels of interest, which will be consistent across the frames of the video, resolving the language inconsistency of stage 1. We achieve this by prompting an LLM, namely Llama-2 (Touvron et al., ), as described next.

GLaMM generates long captions which may contain unnecessary details that can distract the LLM. To address this, we first extract Subject-Verb-Object triplets (SVO) from the frame-level captions as well as adpositions and adpositional objects using Part-of-Speech (POS) tagging. The input to the LLM are the SVO triplets for each frame of the video. By doing so, we feed the LLM with visual knowledge, as seen by the grounded captioning model, that represents the subjects, the actions that they are performing and the objects that are used to perform the action or the objects to which the action is applied. We perform in-context learning with the LLM by feeding it with example pairs of frame-level SVO triplets and the expected video-level captions associated with them. Please see the Supplementary material for the in-context learning prompt that we provide to the LLM. Then, given a new SVO triplet, the LLM answers with the predicted caption for the corresponding video and also tags the phrases that correspond to objects of interest within <p></p> tags. An overview of this process is depicted in Figure 2, middle.

Stage 3: Tracking by language. While Stage 2 provides a consistent video-level caption, the phrases that correspond to objects are inconsistent across frames as we have used an image-based model to obtain them. To address this issue, we propose to associate the frame-level phrases with the video-level phrases. We name this procedure tracking by language, as we use only the textual description of the phrases without any visual information from the area within the corresponding bounding boxes, and the result is consistent labelling of the bounding boxes throughout the video using the video-level phrases as the labels. Then, bounding boxes with the same label across frames are grouped into video object tracks. We formulate this as a classification problem and prompt again the LLM to solve it with in-context learning. The in-content examples fed to the LLM consist of the input frame-level phrase to be classified and the video-level phrases that make the set of classes for the given video. Please see the Supplementary material for the prompt used for this step. A summary of this process is visualised in Fig 2, right.

With the completion of all three stages, we obtain videos automatically annotated with captions and grounded bounding boxes along with temporally coherent labels that correspond to the phrases from the caption. Additional details and clarifications of our automatic annotation method can be found in Section C in the Appendix.

### 4.2 HowToGround dataset for grounded video caption generation

We introduce HowToGround, an automatically annotated dataset obtained by applying our automatic annotation method to Internet instructional videos from the HowTo100M (Miech et al., ) dataset. The HowToGround dataset consists of videos and automatically annotated captions along with temporally consistent bounding boxes across frames grounded to phrases from the captions.

Data Generation. We use HowTo100M (Miech et al., ) as the video source, a dataset of 100M narrated instructional videos from YouTube, where the humans in the video narrate the actions that they are performing. We choose HowTo100M due to its diversity of actions, scenes, objects and lighting conditions. As in most cases the videos have been captured non-professionally by users, the videos have large viewpoint changes and camera movements, as well as abrupt shot changes. The videos are also of low spatial resolution. These factors along with the diversity of actions, which leads to a long-tailed distribution of events, make the data challenging for grounding.

The narrations of users in HowTo100M have been transcribed by Miech et al. () using ASR, providing text and narration timestamps along with the videos. One might naturally question the need for Stage 1 (video captioning) of our automated curation method, since there is available narrated text. However, the available text is noisy and not suitable for grounding, as in many cases the narrator may thank the viewers, commercials might be part of the narration as well as other instances where the narration is not about the visual environment and its associated objects, e.g. instructions which cannot be grounded in the video. Moreover, the timestamps are noisy and do not always correspond to the performed action (see Han et al. ()). To alleviate this, we use the timestamps from HowToCaption (Shvetsova et al., ), where the authors prompted LLMs to aggregate the narrations to generate captions and predict more meaningful timestamps for the associated predicted captions.

We randomly sample 50k video clips from HowTo100M videos using start/end timestamps from HowToCaption. The videos from HowTo100M have variable frame rates usually ranging in 25-30 fps, and we downsample them at 5fps. The majority of the clips are 8 seconds long with a spatial resolution of 455$\times$256 pixels. We run our automated annotation pipeline on this set of data to obtain the HowToGround dataset.

Resulting dataset. After rejecting videos for which our proposed automatic annotation method failed, e.g. the LLM has not provided the expected type of answer, we end up with 48.5k automaticaly annotated videos. We use these videos along with the automatic annotations to train the VideoGround grounded video caption generation model, which we describe next.

Figure: Figure 3: An overview of our VideoGround grounded caption generation model. The key technical innovations enabling grounded caption generation in video are outlined by dashed red rectangles and include: (i) spatio-temporal adapters; (ii) the bounding box decoder and (iii) the temporal objectness head.
Refer to caption: x3.png

## 5 VideoGround: A Model for Grounded Video Caption Generation

We propose VideoGround, a video LMM for the GROC task, see Figure 3. We take inspiration from the GLaMM (Rasheed et al., ) model due to its state-of-the-art performance for image-based Grounded Caption Generation. As video LMMs require large amounts of data for training, we build on a pretrained version of GLaMM. The novel components of our proposed VideoGround model are (shown in dashed red rectangles in Figure 3): i) the spatio-temporal adapters with pooling which enable modelling temporal information efficiently, ii) the bounding box decoder which allows to re-use the large-scale pretrained decoder weights of GLaMM and iii) the temporal objectness head for modelling objects that temporally leave the frame. We ablate these components in Table 3 and demonstrate their key importance.

Overview of the architecture. Figure 3 shows the different components of our approach. The Global Video Encoder, $\mathcal{V}_{e}(\cdot)$, outputs video features, $o_{e}$, which are pooled spatio-temporally, resulting in the video prompts. These are projected to a language embedding space with $VL(\cdot)$. A prompt consisting of video-language tokens is ingested by the LLM, $\mathcal{LM}(\cdot)$, which is prompted to generate a caption for the video by tagging the phrases that correspond to objects and appending them with detection tokens (shown with red and green in the LLM’s generated caption in Figure 3). The LLM’s output hidden states that correspond to the generated caption are projected to queries (using $LQ(\cdot)$). The queries that correspond to detection tokens are fed to the bounding box decoder, $D(\cdot)$. The Grounding Video Encoder, $\mathcal{V}_{g}(\cdot)$, outputs fine-grained video features, which are also fed to the decoder. The decoder performs cross-attention frame-wise between the queries and the outputs of $\mathcal{V}_{g}(\cdot)$, $o_{g}$, which are used as keys/values. Finally, the prediction heads output bounding box predictions and temporal objectness scores for each object at each frame. This objectness score is used to predict the presence/absence of the object in each video frame and is of major importance for the grounded video caption generation task. For details about the visual backbones $\mathcal{V}_{e}(\cdot)$ and $\mathcal{V}_{g}(\cdot)$ as well as details about the LLM $\mathcal{LM}(\cdot)$ including the format of its multimodal inputs and its vocabulary, please see Section B in the Appendix.

Spatio-temporal adapters and pooling. We obtain $\mathcal{V}_{e}(\cdot)$ and $\mathcal{V}_{g}(\cdot)$ by adapting the respective pretrained image encoders of GLaMM. We achieve this by interleaving spatio-temporal adapter layers between the original encoder layers. To stabilise training, we add residual connections and introduce a learnable parameter that is multiplied by the adapter’s output and starts from 0 at the beginning of training. By doing so, at the beginning of training the adapter’s output is effectively cancelled out and the network observes only the original encoder’s output. As training progresses, the learnable parameter is tuned and the network automatically adjusts the contribution of the adapter based on the gradients of the loss. A similar procedure has also been employed by Flamingo (Alayrac et al., ) where it has been shown that it helps for a more stable training. An adapter layer performs $a(o)=o+tanh(\alpha)\times f(o)$,
where $o$ is the output of the preceding encoder layer, $\alpha$ is the tunable parameter that is initialised to 0 and passes through a $tanh$ activation and $f(\cdot)$ is the adapter layer.
As feeding the full video tokens $o_{e}$ to the LLM is computationally prohibitive, we introduce a spatio-temporal pooling function after the output of $\mathcal{V}_{e}(\cdot)$, i.e., $o_{p}=p(o_{e})$.

Projection layers. We project the outputs of the Global Video Encoder and the output hidden states of the LLM with MLPs, $o_{p^{\prime}}=VL(o_{p})$ and $o_{q}=LQ(o_{l})$, where $VL(\cdot)$ projects the visual features to an embedded language space, while $LQ(\cdot)$ projects the LLM’s hidden states to queries. $o_{p^{\prime}}$ is the LLM’s visual input while $o_{q}$ is input to the bounding box decoder that is described next.

Bounding box decoder and prediction head. We re-purpose the decoder of GLaMM for bounding box decoding. This allows us to leverage the pretrained weights of the decoder. GLaMM’s decoder follows the decoder architecture of SAM (Kirillov et al., ) and is designed for segmentation mask decoding. It uses the visual features of the Grounding Image Encoder as queries and the embedded segmentation tokens as keys/values and applies cross-attention to them, resulting in an output of the same dimensionality as the input visual features that is used for predicting the masks. We transform the mask decoder to a bounding box decoder by using the embedded detection tokens as queries, and the visual features of the Grounding Video Encoder as keys/values, resulting in an output that has same length as the detection tokens, allowing us to predict a bounding box for each detection token that corresponds to a noun phrase in the caption. Importantly, while $\mathcal{V}_{g}(\cdot)$ performs video processing, we apply the cross-attention in a frame-wise fashion to predict objects at each frame of the input video. Formally, the bounding box decoder performs: $o_{d}=D(o_{g},\mathbbm{1}_{\{o_{q}=<DET>\}})$, where $o_{d}\in\mathbb{R}^{T\times N_{d}\times D}$, $N_{d}$ is the number of detection tokens predicted by the LLM, $D(\cdot)$ is the decoder, and $\mathbbm{1}_{\{o_{q}=<DET>\}}$ is an indicator function selecting only the embedded language tokens, $o_{q}$, that correspond to detection tokens. We employ a bounding box prediction head on the output of the decoder, $o_{d}$. It is an MLP that predicts bounding box coordinates for the embedded detection tokens at each frame: $p_{bb}=h_{bb}(o_{d})$, where $p_{bb}\in\mathbb{R}^{T\times N_{d}\times 4}$ are the bounding box predictions and $h_{bb}(\cdot)$ is the bounding box head.

Temporal objectness head. As discussed before, one major challenge for videos is that objects might disappear and reappear in different frames of the video. To address this, we introduce a temporal objectness head. Different than objectness predictions in image-based object detection, the purpose of this head is to predict whether an object is visible or not at a given frame of a video: $p_{tobj}=h_{tobj}(o_{d})$, where $p_{tobj}\in\mathbb{R}^{T\times N_{d}\times 1}$ are the temporal objectness scores and $h_{tobj}(\cdot)$ is the temporal objectness head. During inference, we threshold $p_{tobj}$ and for each frame we select only the bounding boxes for which the temporal objectness scores pass the threshold.

Loss function. Our loss function is a combination of a language modelling loss for captioning, two spatial losses relevant to video object detection and a temporal objectness loss. Their details can be found in Section B.

## 6 Experiments

Implementation details. All implementation details including architectural choices of VideoGround as well as training/inference details can be found in Section B in the Appendix.

**Table 1: Performance comparison of the proposed VideoGround model, the proposed pseudolabelling method and the GLaMM (Rasheed et al., ) baseline on our human-annotated test set for the Grounded Video Caption Generation task. In the frame-level set-up performance metrics are calculated across all frames of all videos in a similar manner as done in a still-image evaluation. In the video-level set-up the metrics are calculated per video and averaged across videos. Improvement / decline in comparison to GLaMM shown in parenthesis.**
|  | Method | METEOR | CIDER | AP50 | mIOU | Recall |
| --- | --- | --- | --- | --- | --- | --- |
| Frame-level | GLaMM | 11.9 | 29.9 | 20.8 | 13.2 | 19.6 |
| Pseudolabelling | 13.8 (▲ 1.9) | 40.0 (▲ 10.1) | 20.6 (▼ 0.2) | 15.1 (▲ 1.9) | 18.2 (▼ 1.4) |  |
| VideoGround | 14.2 (▲ 2.3) | 46.8 (▲ 16.9) | 25.2 (▲ 4.4) | 17.5 (▲ 4.3) | 21.9 (▲ 2.3) |  |
| Video-level | GLaMM | 11.9 | 29.9 | 26.6 | 13.1 | 22.0 |
| Pseudolabelling | 13.8 (▲ 1.9) | 40.0 (▲ 10.1) | 27.1 (▲ 0.5) | 15.1 (▲ 2.0) | 20.4 (▼ 1.6) |  |
| VideoGround | 14.2 (▲ 2.3) | 46.8 (▲ 16.9) | 33.7 (▲ 7.1) | 17.4 (▲ 4.3) | 24.6 (▲ 2.6) |  |

Results. The results on our human annotated test set are presented in Table 1. We compare results of the proposed VideoGround model with the pseudolabelling method (section 4) and (still image-based) GLaMM by running the pseudolabelling and GLaMM on the test set. The pseudolabelling method is a natural fit for a baseline for this task as it performs image-based grounded captioning followed by video-level aggregation with LLMs without any training. Moreover, GLaMM helps to assess the benefits of our method in comparison to still-image grounding. For GLaMM, we select the caption of the center frame of each video as the video-level caption.

This evaluation provides two useful insights. First, our pseudolabelling method improves over GLaMM, where the improvement is significant for captioning with the pseudolabelling method obtaining 10.1 points higher CIDER score. This demonstrates the importance of the video-level caption aggregation (2nd stage of the pseudolabelling). The recall of our pseudolabelling decreases in comparison to GLaMM. This is natural because GLaMM predicts labels for the bounding boxes independently per frame, increasing the chance of correctly matching the ground truth labels in each frame, leading to a higher number of true positives (as discussed in Section 3, recall considers both predicted bounding boxes and embeddings of predicted labels). Nevertheless, this comes at the cost of temporally inconsistent labelling and therefore inability to track objects, which is resolved by the 3rd stage of our pseudolabelling pipeline.

Second, VideoGround advances the results further with significant margins across the board.
Importantly, VideoGround outperforms significantly vanilla GLaMM on AP50 (7.1 points in video-level and 4.4 points in frame-level evaluation) and mIoU (4.3 points); while it improves in recall over the pseudolabelling method that suffers in this metric (4.2 points and 3.7 points in video-level and frame-level evaluation respectively). Lastly, VideoGround performs better in captioning too.
These results indicate that there is a subtle interplay between our proposed pseudolabelling method and VideoGround which is critical for improving the captioning and grounding accuracy of the model as well as the ability of the model to predict the presence of objects in the video both as a natural language description and as a spatio-temporally grounded bounding box. Components of VideoGround that contribute substantially towards this are: i) the temporal objectness, ii) the adapters, iii) freezing the pretrained models to maintain the rich knowledge acquired from GLaMM pretraining while fine-tuning only key parts of the model. We ablate these choices next.

Ablations. Tables 3 and 3 compare variants of our VideoGround model.
In particular, in Table 3 we ablate the adapters, the temporal objectness and unfreezing parts of the model. The importance of each of those components is evident as removing each component causes performance degradation. Specifically, unfreezing key parts of the architectures and employing the adapters have a significant impact on the model’s performance particularly for CIDEr, AP50 and recall. We can conclude that spatio-temporal modelling using the adapters is important for grounded video caption generation, probably because it allows to learn holistic video semantics through the Global Video Encoder and to produce spatio-temporally consistent representations through the Grounding Video Encoder. Last but not least, temporal objectness provides great benefits in AP50 and mIoU, demonstrating the significance of modelling objects that temporally leave the frame in our GROC dataset. This is at the expense of recall – turning off the temporal objectness is equivalent to predicting bounding boxes for each object in the caption in every frame, retrieving more positive instances while introducing a significant number of false positives as it is evident by AP50.
In Table 3, we examine which parts of the model are beneficial to be fine-tuned. We opt to keep the visual backbones and LLM frozen to reduce overfitting and retain the rich knowledge learnt from GLaMM pretraining. Note, that in every case we fine-tune the embedding and output layers of the LLM since we modify its vocabulary with special tokens, as well as the bounding box and temporal objectness heads as they are necessary for the grounded video caption generation task. We observe that fine-tuning the decoder and VL projection layer is beneficial while fine-tuning the LQ layer in addition results in slightly worse performance. That is probably because the decoder is of central importance
for detection while the VL projection transforms the video representation in a format that is comprehensible by the LLM.
Additional ablation regarding the automatic annotation approach to obtain the pseudolabels is in the appendix (Table 7, Section D) and demonstrates that our automatic annotation method is general enough and robust under different grounding models to generate frame-level label (Stage 1).

Qualitative Results. We show qualitative results of VideoGround on two example videos in Figure 4. More qualitative results can be found in Figures 6 and 7 in the Appendix. We showcase four important properties of VideoGround. First, VideoGround is able to produce video-level natural language captions describing the main action in the video. Second, VideoGround can ground multiple objects. Third, the model produces spatio-temporally smooth predictions. Finally, in the third and fifth example in Figure 7, we demonstrate that temporal objectness models objects that temporally leave the frame, as it does not predict a bounding box for the hand when the hand disappears.

**Table 2: Ablations of model components: Video-level evaluation on the validation set. AD: adapters, TO: temporal objectness.**
| Unfreeze | AD | TO | METEOR | CIDEr | AP50 | mIOU | Recall |
| --- | --- | --- | --- | --- | --- | --- | --- |
| ✗ | ✓ | ✓ | 12.6 | 53.5 | 32.2 | 18.0 | 23.8 |
| ✓ | ✗ | ✓ | 12.8 | 50.8 | 32.3 | 18.6 | 23.7 |
| ✓ | ✓ | ✗ | 13.4 | 57.7 | 30.9 | 15.6 | 27.0 |
| ✓ | ✓ | ✓ | 13.4 | 57.7 | 36.3 | 18.9 | 25.1 |

## 7 Conclusion

We have proposed a new task, grounded video caption generation, together with a new manually annotated test set, which we refer to as GROC.
We have also introduced a new automatic annotation method and pseudo annotated a large-scale training dataset of instructional videos, which we call HowToGround. We have designed a new grounded video caption generation model and trained the model on the HowToGround dataset.
The obtained results validate the new training dataset and model by demonstrating their benefits over still-image grounding baselines setting state of the art for the new task of grounded video caption generation.
We believe the new training and test datasets can spark further interest of the research community in this problem.

Limitations.
The model may inherit biases of the still image GLaMM model employed to generate our automatic pseudo annotations and also used for initialization. Combining the automatic pseudo annotations together with additional manual training annotations would open up the possibility for Internet-scale training of grounded video caption generation models.

Figure: Figure 4: Qualitative examples showing the predictions of VideoGround on two videos. The examples showcase three important properties of VideoGround: i) it is able to produce video-level natural language captions describing the main action in the video, ii) it can ground multiple objects, iii) it produces spatio-temporally consistent predictions. More qualitative results can be found in Figures 6 and 7 in the Appendix. In the third and fifth example in Figure 7, we demonstrate another property of VideoGround: iv) temporal objectness models objects that temporally leave the frame as it does not predict a bounding box for the hand when the hand disappears.
Refer to caption: x4.png

#### Acknowledgments

This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140). This research also received the support of projects EXA4MIND, ELIAS and ERC FRONTIER, funded by the European Union’s Horizon Europe Research and Innovation Programme, under Grant Agreements N°101092944, N°101120237 and N°101097822. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Finally, this research was funded in part by the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), and the ANR project VideoPredict, reference ANR-21-FAI1-0002-01.

## Appendix

## Appendix A Dataset Statistics And Comparison with Other Datasets

### A.1 Dataset Statistics

Table 4 reports the statistics of both the HowToGround dataset and the GROC test set. Word clouds of the natural language descriptions for the HowToGround dataset and the GROC test set are shown in Figure 5.

**Table 4: Statistics of HowToGround and GROC datasets.**
| Statistic | HowToGround | GROC |
| --- | --- | --- |
| Avg num frames | 44.6 | 40.0 |
| Avg duration (seconds) | 7.9 | 8.0 |
| Avg num instances per video | 80.1 | 118.8 |
| Total num instances | 3,883,707 | 118,775 |
| Avg box width $\times$ height | 243.2 $\times$ 172.5 | 162.0 $\times$ 132.5 |
| Avg tube length (frames) | 6.4 | 29.8 |
| Avg caption length (words) | 12.2 | 13.7 |

Figure: (a)
Refer to caption: x5.png

### A.2 Comparison with Other Datasets

Tables 5 and 6 compare our HowToGround and GROC datasets, respectively, with other datasets across various axes. Since HowToGround is designed for training, in Table 5 we compare it with datasets that have training sets. In the same manner, since we propose GROC as a test set, in Table 6 we compare it with datasets that provide a test set.

Both HowToGround and GROC are the only datasets that simultaneously have multiple annotated objects per frame, annotations across multiple frames and annotated noun phrases from the caption that are linked to object bounding boxes, making them the only suitable datasets for the proposed grounded video caption generation task. In Table 5, the only other dataset with multiple objects per frame and annotated noun phrases is ActivityNet-Entities. Nevertheless, ActivityNet-Entities provides annotations for a single frame per video segment while HowToGround’s training set has an average of 43.5 annotated frames per video segment. GROC is the only test dataset with multiple annotated objects per frame and annotated noun phrases, while it provides the longest captions and has the second largest number of total annotated instances.

**Table 5: Comparison of the proposed HowToGround dataset with other training datasets for grounding.**
| Dataset | Avg. caption length | Avg. annot. frames | Multiple objects | Phrases | Videos | Total annot. instances |
| --- | --- | --- | --- | --- | --- | --- |
| VidSTG (Zhang et al., ) | 10.1 | 275.0 | ✗ | ✗ | 36.2K | 9.9M |
| HC-STVG (Tang et al., ) | 17.4 | 147.2 | ✗ | ✗ | 10.1K | 1.5M |
| ActivityNet-Entities (Zhou et al., ) | 13.8 | 1.0 | ✓ | ✓ | 37.4K | 93.6K |
| HowToGround (Ours) | 12.1 | 43.5 | ✓ | ✓ | 48.5K | 3.9M |

**Table 6: Comparison of the proposed GROC dataset with other test datasets for grounding.**
| Dataset | Avg. caption length | Avg. annot. frames | Multiple objects | Phrases | Videos | Total annot. instances |
| --- | --- | --- | --- | --- | --- | --- |
| VidSTG (Zhang et al., ) | 10.1 | 262.3 | ✗ | ✗ | 4.6K | 1.2M |
| YouCook-Interactions (Tan et al., ) | 8.6 | 9.8 | ✗ | ✗ | 0.25K | 2.5K |
| GroundingYT (Chen et al., ) | 2.0 | 4.1 | ✗ | ✗ | 4.2K | 17.3K |
| GROC (Ours) | 13.2 | 43.2 | ✓ | ✓ | 1K | 119K |

## Appendix B Details of the VideoGround model

Backbones. VideoGround consists of two video encoders and a multimodal LLM as its main backbones.
The Global Video Encoder $\mathcal{V}_{e}(\cdot)$, takes as input a video $v\in\mathbb{R}^{T\times H1\times W1}$ and produces an output $o_{e}\in\mathbb{R}^{T\times\frac{H1}{p}\times\frac{W1}{p}}$, where $p$ is the patch size of the underlying visual transformer. Its purpose is to provide a holistic representation of the video that will be ingested by the LLM. The Grounding Video Encoder $\mathcal{V}_{g}(\cdot)$, takes as input a video $v\in\mathbb{R}^{T\times H2\times W2}$, where $W2>W1$ and $H2>H1$. It produces $o_{g}\in\mathbb{R}^{T\times\frac{H2}{p}\times\frac{W2}{p}}$. $o_{g}$ is used to ground phrases from the caption to the visual content, which is performed by the bounding box decoder that is described later. The input video to the Grounding Video Encoder is of larger spatial resolution than that of the Global Video Encoder for enhanced localisation capability. Finally, the LLM $\mathcal{LM}(\cdot)$ takes as input a multimodal sequence $s\in\mathbb{R}^{L\times D}$ and produces an output $o_{l}$ of the same size. Its input is of the form The <video> provides an overview of the video. Could you please give me a description of the video? Please respond with interleaved bounding boxes for the corresponding parts of the answer. <video> is replaced by the output of $\mathcal{V}_{e}(\cdot)$, and therefore the LLM ingests mixed language and visual tokens. We also augment the LLM’s vocabulary with a detection token <DET>, prompting the model to generate responses with <DET> tokens by the phrases that correspond to objects to be detected in the video.

Loss function. Our loss function is a combination of a language modelling loss and losses relevant to video object detection. The language modelling loss is a Cross-Entropy loss applied on $o_{l}$. For object detection, we follow DETR (Carion et al., ) and use a gIoU loss (Rezatofighi et al., ) and an L1 loss applied on $p_{bb}$. Different than Carion et al. (), the losses are applied per frame and summed over frames. Moreover, the losses are applied only to the objects that appear in the frame (rather than each object in the caption) using the ground-truth temporal objectness scores. The representation that we use for the bounding boxes is [x,y,w,h] and their coordinates are normalised with the dimensions of the video. Finally, we employ a binary cross-entropy loss on $p_{tobj}$. Our loss is, hence, defined as:

$$ $\displaystyle\mathcal{L}_{LM}$ $\displaystyle=CE(o_{l}),\quad\mathcal{L}_{gIoU}=gIoU(p_{bb},gt_{bb}),\quad \mathcal{L}_{L1}=L1(p_{bb},gt_{bb}),\quad\mathcal{L}_{tobj}=BCE(p_{tobj},gt_{ tobj})$ (1) $\displaystyle\mathcal{L}$ $\displaystyle=\lambda_{LM}\times\mathcal{L}_{LM}+\lambda_{gIoU}\times\mathcal{ L}_{gIoU}+\lambda_{L1}\times\mathcal{L}_{L1}+\lambda_{tobj}\times\mathcal{L}_{ tobj}\text{,}$ (2) $$

where $gt_{bb}$ are the ground truth boxes and $gt_{tobj}$ are the ground truth objectness scores and $\lambda$ are the weights for the losses.

Training/inference. We realise the Global Video Encoder $\mathcal{V}_{e}(\cdot)$ with a CLIP-L (Radford et al., ) model with an input of 336$\times$336 and a patch size of 14. The Grounding Video Encoder $\mathcal{V}_{g}(\cdot)$ is instantiated with a SAM (Kirillov et al., ) encoder and the bounding box decoder $D(\cdot)$ is a SAM-based decoder, the same as in GLaMM (Rasheed et al., ).
The LLM $\mathcal{LM}(\cdot)$ is a Vicuna-7B model (Chiang et al., ). During training we keep $\mathcal{V}_{e}(\cdot)$, $\mathcal{V}_{g}(\cdot)$ and $\mathcal{LM}(\cdot)$ frozen. $\mathcal{V}_{g}(\cdot)$ originally takes as input 1024$\times$ 1024 images. As this is too large to fit in memory for videos, we instead use 512$\times$512 video spatial resolution, while we interpolate the positional encodings of $\mathcal{V}_{g}(\cdot)$ and fine-tune them. Adapters are 3D spatiotemporal convolutional layers with a kernel of size $3\times 3\times 3$ and a stride of 1. We apply adapters to every 3 layers of $\mathcal{V}_{e}(\cdot)$ and to all global attention layers of $\mathcal{V}_{g}(\cdot)$. The bounding box head $h_{bb}$ is an MLP with two FC layers and a ReLU activation function in between, while the temporal objectness head $h_{tobj}$ is a linear layer. Both prediction heads employ a sigmoid activation function. We apply a threshold of 0.5 to the temporal objectness scores. Both the adapters and the prediction heads are randomly initialised. We use $T=8$ frames for the videos during both training and testing. During training we perform random sparse sampling of frames by splitting the video in 8 segments and randomly drawing a frame from each segment while during testing we pick the centre frame of each segment.

We train VideoGround for 10 epochs using a batch size of 128. We use a learning rate of $10^{-4}$ with warmup for the first 100 training steps and linearly decay the learning rate for the rest of training. We do not apply any weight decay or spatial data augmentation. We use $\lambda_{LM}=\lambda_{gIoU}=\lambda_{L1}=\lambda_{tobj}=1$.

## Appendix C Details of the automatic annotation method

Multiple people in the video. Our automatic annotation method can handle multiple subjects in a video as long as one of the two following conditions are met: a) the subjects are described with a distinct language, e.g. ‘man with green jumper’ and ‘man with blue shirt’, or b) the subjects are within a Subject-Verb-Object relationship even when described with the same terms, e.g. (‘person’, ‘dances’, ‘with’, ‘person’) which would produce ‘A person dances with another person’. If neither conditions are met, the caption aggregation (Stage 2) may merge the two subjects into one.

Association of verbs and objects is naturally performed through the Subject-Verb-Object triplets. For example, given two relationships: (‘man’, ‘cuts’, ‘onions’) and (‘woman’, ‘stirs’, ‘food’, ‘in’, ‘pot’). The LLM-based caption aggregation step (Stage 2) has sufficient information to associate the man with the action of cutting the onions and the woman with stirring the food.

Additional details of Stage 3. We provide additional details of the procedure of Stage 3 using the example from Fig. 2, right. The object in the woman’s hands is described as ‘a green beverage’, ‘a glass’, and ‘a glass of green liquid’ across different frames. Stage 2 has provided the video-level noun phrases ‘a woman’ and ‘a beverage’. Stage 3 is formulated as a classification problem where each one of ‘a green beverage’, ‘a glass’, and ‘a glass of green liquid’ are the inputs to be classified in one of the classes {‘a woman’, ‘a beverage’, $\emptyset$} and thus associated with the right bounding box. The class $\emptyset$ represents the “None” class, i.e. when an input does not belong to any of the known classes and it is useful for noisy inputs.

## Appendix D Additional Experiments

We show below that our pseudolabeling approach is general enough and works with different grounding models to generate frame-level labels. This is important as it can increase the variability of the outputs and can make the output less prone to biases from a single frame-level grounding model.

To demonstrate the generality of our pseudolabelling method, we have replaced GLaMM from Stage 1 with an alternative grounding method. To this end, we’ve incorporated a GIT approach (Wang et al., ) for captioning, Llama3 (Dubey et al., ) for extraction of noun phrases from the caption, and the open vocabulary detector OWLv2 (Minderer et al., ) for detecting and localizing the noun phrases in a given frame. Similarly to the GLaMM approach, the outputs of Stage 1 are: a) frame-level captions, b) groundable noun phrases from the captions and c) bounding boxes for each phrase. The quantitative results of this new frame-level grounding approach can be found in Table 7 where we compare the performance of the original pseudolabelling method with this alternative on the validation set. We observe that overall this alternative pseudolabelling method maintains a reasonable performance, demonstrating that the subsequent steps of our approach can handle outputs of different pseudolabelling methods fairly well. Nevertheless, pseudolabelling with our proposed Stage 1 performs noticeably better in AP50 and Recall, as GLaMM has been explicitly trained for grounding, which is not the case for GIT, Llama3 and OWLv2, which can underperform for various reasons, including Llama3 extracting non-groundable noun phrases or OWLv2 missing objects. At the same time, pseudolabelling with the alternative Stage 1 performs slightly better for the captioning metrics and mIoU due to the superior performance of GIT for captioning and OWLv2 for object detection.

Given the complementary benefits of the proposed and alternative Stage 1, one can improve the pseudolabels by combining the outputs of the two approaches, which we leave for future work.

**Table 7: Comparison of our pseudolabeling approach with the proposed Stage 1 for grounding vs. an alternative approach for grounding in Stage 1 based on GIT (Wang et al., ), Llama3 (Dubey et al., ) and OWLv2 (Minderer et al., ).**
| Method | METEOR | CIDER | AP50 | mIOU | Recall |
| --- | --- | --- | --- | --- | --- |
| Pseudolabelling w. proposed Stage 1 | 12.5 | 43.8 | 23.4 | 15.5 | 18.8 |
| Pseudolabelling w. alternative Stage 1 | 12.9 | 45.0 | 18.9 | 16.9 | 14.3 |

## Appendix E Qualitative results

Figures 6 and 7 show qualitative results of our VideoGround model (Section 5 in the main paper) on the test set. These examples demonstrate the following properties of our VideoGround model: i) it is able to produce video-level natural language captions describing the main action in the video, ii) it produces spatio-temporally consistent bounding boxes, iii) it can ground multiple objects in the video, iv) it can deal with objects that temporally leave the frame using the proposed temporal objectness – in the third and fifth example in Figure 7, it does not predict a bounding box for the hand when the hand disappears.

Figure: Figure 6: Qualitative results of our VideoGround model (Section 5 in the main paper) on the (unseen) test set. The color coded sentence fragments are spatio-temporally localized in the video with the bounding boxes color coded with the same color. Our model is able to produce video-level natural language captions describing the main action in the video together with spatio-temporally consistent bounding boxes grounding multiple objects in the video.
Refer to caption: x7.png

Figure: Figure 7: More qualitative results of our VideoGround model (Section 5 in the main paper) on the (unseen) test set. In the third and fifth example, we demonstrate the ability of VideoGround to model objects that temporally leave the frame using the proposed temporal objectness, as it does not predict a bounding box for the hand when the hand disappears.
Refer to caption: x8.png

## Appendix F Protocol for human annotations

Below we describe the annotation guidelines for annotating our validation/test sets.

Annotation Guidelines:

- 1.
Video Selection:
•
You will be provided with a larger set of videos than needed.
•
Your first task is to select clips that are considered ‘interesting’ based on criteria that will be discussed further. An ‘interesting’ video typically includes dynamic events or actions that are clear and distinguishable despite the low video quality. In those events/actions people usually interact with objects, e.g. ‘A man is cutting an onion using a knife’. ‘Non-interesting’ events are typically static, e.g. a person simply standing/sitting and talking. Non-interesting events are also events with ambiguous actions taking place, i.e. generic/abstract actions that cannot be described concisely or actions for which the annotator is unsure about what is happening in the video.
- 2.
Video Annotation:
•
For each selected video clip, write a concise, one-sentence description of the main event taking place in the clip. If the action is too complex, use at most two sentences for describing it, but prioritise one-sentence descriptions.
•
Focus only on the objects that humans interact with rather than describing densely every object in the scene.
•
To enrich the language descriptions, also describe properties of objects such as color, shape, etc, e.g. ‘blue cup’ or ‘red onion’. It is not strictly necessary to always describe the object’s property but only when deemed important by the annotator.
•
When you are unsure about the object being used, you can simply describe it as ‘object’. If object is unknown but the category of the object is known, please describe the object using its category, e.g. ‘food’.
•
When there are two or more humans in the scene, use one of their characteristics to distinguish them, e.g. ‘the woman in the red shirt standing next to the woman in the green shirt is putting a strawberry on a cocktail glass’.
•
If there are multiple actions happening consecutively, describe all of them and their associated objects. E.g. ‘a person is doing action-1 using object-1, then doing action-2 with an object-2’. As shown in the example, you can use ‘then’ for connecting temporally adjacent actions.
•
Provide bounding boxes for humans/different objects mentioned in your description. These bounding boxes should be applied to all frames where the objects are visible.
•
Label each bounding box with a short phrase directly from your sentence description (e.g., ‘a brown dog’, ‘personś hands’).
•
It is not necessary that each object appears in each frame of the video. For example, a person might be using a tool, then leaving it down and using another tool. In this case, you would annotate with bounding boxes the first tool for the first half of the video and the second tool for the second half. Another common case is that objects or the person might disappear and then reappear. In this case, again all instances of the object must be annotated, so you should be careful about objects leaving the scene as they might enter the scene again later.
•
If there are many small objects, e.g. mushrooms in a pan, use a single bounding box labelled as ‘mushrooms’.
•
There are cases where two or more bounding boxes are needed for objects of the same type: a) one bounding box for each human hand when both are used to perform an action, b) one bounding box for each tool/container/appliance etc of the same type that the human is using, e.g. when they are placing food in two dishes, or pouring the content of a shaker in two cocktail glasses.
•
Descriptions: Must be accurate and written in fluent English. Suitable for either native speakers or highly proficient English speakers.
•
Bounding Boxes: Ensure that bounding boxes accurately encompass the objects for the entirety of their visibility within the clip. The bounding boxes should be consistent and smooth across frames, maintaining size and position as closely as possible given the movement of the object and video quality. An exception is when there are abrupt viewpoint changes of the camera, which might result in objects abruptly changing position and size across neighbouring frames.

The annotation criteria have been extensively discussed with the annotation provider and the annotators have been trained based on those criteria prior to commencing the annotation process. We have also performed a pilot annotation project with the annotation provider on 10 video clips with several rounds of careful checking and feedback. Moreover, the annotation provider performed regular quality reviews on the annotations to ensure that the annotation criteria have been met.

## Appendix G Prompts for automatic
curation of spatio-temporally grounded captions

The full prompt for the Stage 2 (Video-level caption aggregation) of our pseudolabeling approach (Section 3 in the main paper) is shown in Figure 8 and the full prompt for Stage 3 (Tracking by language) in Figure 9.

Figure: Figure 8: The full prompt for Stage 2 (Video-level caption aggregation) of our pseudolabeling approach (Section 3 in the main paper).

Figure: Figure 9: The full prompt for Stage 3 (Tracking by language) of our pseudolabeling approach (Section 3 in the main paper).

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Clarification on Novelty: Needs a clearer explanation of differences from previous work, specifically the distinct contributions beyond those in “Grounded Video Description” (Zhou et al., 2019).
soundness: 2
strengths: Novel Task, Dataset, and Model: The paper introduces a new task with a manually annotated evaluation set and a large-scale automatically generated dataset.
Dataset build up: Uses LLMs to generate consistent video-level captions from frame-level annotations, improving temporal consistency.
Model that support vlm to output bbox for video: VideoGLaMM incorporates spatio-temporal adapters and temporal objectness, supporting consistent tracking of objects in video.
summary: The paper proposed a new task (GROC), a manually annotated test dataset, and an automatically generated dataset. It introduces VideoGLaMM, a model designed to generate captions and track object bounding boxes over time. Key features include spatio-temporal adapters, a bounding box decoder, and a temporal objectness head, all aimed at bridging multimodal understanding for video and language.
weaknesses: Limited Model Comparison: It would be better for VideoGLaMM to include comparisons against additional models like InternVL and Qwen-VL, which also produce bounding boxes in text format, to strengthen its evaluation. Since the paper aims to establish a new task, a more comprehensive benchmark would enhance the reliability of its proposed contributions.

Discussion Scope: Additional discussion is needed on alternative methods for VLM bounding box outputs, specifically addressing why GLaMM was prioritized over other methods like [1,2]. This would help clarify its selection rationale within the broader context of existing approaches.

[1] Bannur, Shruthi, et al. "MAIRA-2: Grounded Radiology Report Generation." arXiv preprint arXiv:2406.04449 (2024).
[2] Zou, Ke, et al. "MedRG: Medical Report Grounding with Multi-modal Large Language Model." arXiv preprint arXiv:2404.06798 (2024).

---

confidence: 5
contribution: 2
initial_rating: 3
presentation: 2
questions: Most of my major concerns can be seen in the weakness section. Here I leave a few more:
1. Line 229-230 Sec. ?? -> Reference is missing.
2. The baselines are insufficient. One simple strategy is to directly feed the frames with grounding boxes into a video LLM. This is a widely used method called visual prompting, which can be tested in this training dataset without any additional structure or model tuning.
3. The ablation study is not convincing to me. As shown in Figure 3, I think most of the audience would like to know whether including grounded information really helps with video captioning; that is, a comparison of removing and keeping the grounding video encoder.
soundness: 2
strengths: 1. The released training and evaluation datasets could be useful in the field of video captioning after addressing the necessary ethics concerns.
summary: This submission gives a task, dataset, and model for grounded video caption generation. The main contributions are:

* They define the task as GROunded Video Caption Generation (GROC) and create a manually annotated test dataset specifically for this task.
   
* They design an automatic annotation pipeline that uses an existing model for grounding and a LLM for frame-level and further video-level captioning. They use this approach to the randomly selected subset of HowTo100M, and generate the final HowToGround training dataset.

* They propose a VideoGLaMM model, trained on the collected HowTo100M dataset, which achieves best performance on grounded video caption generation.
weaknesses: I would say the most important weakness of this paper is **over-claim, fails to discuss and compare previous published research and show no respect to previous work**. Let's start with the beginning:

1. The authors claim that they propose a new task called grounded video caption generation (GROC). However, I don’t think so at all. Please refer to paper [1] titled "Grounded Video Description." I think that, in vision-and-language research, most researchers use the terms "video description" and "video captioning" interchangeably (the slight difference may be that "video description" sometimes includes more details). Paper [1] is the first to collect grounded video-text datasets and propose a model that uses grounding information to generate better video descriptions. They didn’t even claim that this task is a so-called new task because it is viewed as using alternative guidance to improve video description generation, not as a new task. One quick and intuitive way to compare these two is to check Figure 1 in this submission and Figure 2 in paper [1]. Thus, **I completely reject the claim that this task is newly proposed by the authors of this submission**.

2. The more interesting thing is that the authors actually cited paper [1] but **did NOT mention it at all in the main pages**. Instead, they "secretly" placed it in the Appendix. In my opinion, a paper with so much overlap should be carefully discussed in the related work section, in section 3 where the authors claim their novelty, and also at the end of section 5, where the authors describe how they construct their datasets.

3. As for the experiments, the proposed model is also lacking of novelty. Put more relevant papers here, like [2, 3, 4], which are also not properly cited and discussed in this submission. Thus, **I also clearly reject the claim in line 055-056 that "At the same time, producing natural language video descriptions where the described objects are spatio-temporally grounded with bounding boxes in videos has received much less attention"**. From my understanding, the authors replace the previsous LSTM networks with large language models. For sure, LLMs can get better results than LSTMs. However, these methods and models are not compared in their experiments. I don't believe that, in the era of LLMs, simply replacing the language module in previous methods with an LLM can be considered a big innovation in the overall model structure.

In summary, I clearly think this submission needs major revisions and a complete reframing in order to meet the publication standards of a conference like ICLR.

References:

[1] Grounded Video Description, Zhou et al. CVPR 2019.

[2] Learning to Generate Grounded Visual Captions without Localization Supervision, Ma et al. ECCV 2020.

[3] Attend and Interact: Higher-Order Object Interactions for Video Understanding, Ma et al, CVPR 2018.

[4] Comprehensive Visual Grounding for Video Description, Jiang et al. AAAI 2024.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 4
questions: * L140 mentions "video selection where ‘interesting’ videos are selected." What criteria define a video as "interesting"? Please provide details.
* L262 mentions "After rejecting videos for which our proposed automatic annotation method failed…" How were these videos rejected? Was it done manually or through an automatic process? More details are needed.
* Although the HowToGround dataset is generated from aggregated image-level annotations, this approach may struggle with capturing interactions that require understanding the temporal dimension, such as determining whether someone is walking forwards or backwards. Is this a limitation of your dataset and the resulting model? Quantitative evaluation of actions requiring temporal interactions could strengthen the paper.
* How does the VideoGLaMM generalize to other datasets, apart from the HowToCaption dataset?
soundness: 2
strengths: * The paper tackles the important task of grounded video captioning, which has numerous applications in downstream tasks such as video retrieval and open-world object tracking in videos.
* The writing is clear.
* The paper introduces the HowToGround dataset for grounded video captioning and provides a high-quality, manually annotated test set for the task.
* The paper proposes an architecture for grounded video captioning, building upon a pre-trained grounded image captioning model.
summary: This paper addresses the task of grounded video captioning, where, given a video, a model must generate a caption and provide bounding boxes for the objects appearing in the caption. The authors introduce two datasets for this task: a small, manually annotated test set, and a larger, automatically annotated training set. Finally, they train a grounded video captioning model on this dataset and evaluate it against two baselines.
weaknesses: * The first paragraph of the introduction feels more like a related work section, making it hard to follow. I would suggest starting by clearly stating the task you aim to solve and providing a high-level summary of the existing work.
* The method for automatically creating the dataset uses SVOs, which can lead to important details being omitted from captions, such as the color of the shirt in Figure 2. This omission could hinder downstream tasks. For instance, a model aiming to find videos of orange shirts might miss this information, which is otherwise captured in current captioning models like GlaMM.
* The paper’s main contribution is the creation of the HowToGround dataset. However, the choices made during dataset creation are not justified, nor does the paper include any ablation study of the creation steps. Including such a study, showing alternative choices for each step, would greatly strengthen the paper. For example, how about using SAM-2 or an open-world object detector to identify object locations? How would VideoGLaMM perform if step 3 were omitted from the dataset creation process? How well does the resulting VideoGLaMM for different train set sizes?

Typos/Errors:
* L230 - Section reference is broken.

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confidence: 4
contribution: 2
initial_rating: 3
presentation: 1
questions: See Weaknesses.
soundness: 2
strengths: - An automated grounded video annotation pipeline was designed, significantly reducing annotation costs.
summary: This paper presents a new task: grounded video caption generation, which aims to generate captions for videos while also providing bounding boxes for the objects mentioned in the captions. 
To achieve this, the authors designed an automatic grounded video annotation pipeline. Based on the dataset constructed using this pipeline, the paper trains a model called VideoGLaMM, which performs well on the grounded video caption generation task.
weaknesses: - The authors claim to introduced a new task: grounded video caption generation. However, there is prior research on this task, such as PG-VIDEO-LLAVA[1]. How do the authors justify their assertion of novelty?

[1] PG-Video-LLaVA: Pixel Grounding Large Video-Language Models.

- For videos that contain two or more events, how should the automated annotation pipeline be applied? For example, consider a scenario where a man picks up a knife to chop vegetables and then puts the vegetables into a pot. If the knife and pot appear simultaneously in the frame but the action hasn't progressed to the pot yet, how can we avoid generating a bounding box for the pot prematurely?

- The case studies (e.g., Figure 4) presented by the authors seem overly simplistic. Can VideoGLaMM be effectively applied to videos that involve multiple events?

- There are very few comparative methods included in the study. The authors should compare more methods, such as the integration of video captioning models with object detection models, and PG-Video-LLaVA.

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confidence: 3
contribution: 2
initial_rating: 3
presentation: 2
questions: See above.
soundness: 2
strengths: The paper proposes an interesting and reasonable task of generating captions for videos while simultaneously grounding the corresponding objects.

The paper presents a technically sound framework to achieve GROC.
summary: The paper introduces a task called grounded video caption generation. It presents a manually annotated benchmark comprising 1,100 video clips, as well as a 50k training set created using an automatic annotation pipeline. GLaMM is adapted with video encoders to achieve GROC. Similar to GLaMM, the authors evaluate VideoGLaMM using a collection of video captioning and object detection metrics. The evaluation results indicate that the proposed method outperforms comparable approaches across various downstream tasks.
weaknesses: I am primarily concerned with the automatic data generation pipeline. In this process, the authors used GLaMM to generate frame-wise captions and then converted these into Subject-Verb-Object (SVO) triplets. Since there is not alignment between generated SVOs, if there are two different instances with the same label in different frames, it is possible that the LLM might treat them as a single instance, potentially leading to inaccurate conclusions and relationships between them.

Similarly, in the tracking by language process, instances are tracking with only textual description of the phrases. This cannot guarantee 2 different instances with same label are mistakenly treated as the single instances. For example, imagining a video that there are 2 different people appearing in different frames but all drinking beers, the process is likely to link them as one person by just considering its textual description. The strategy described in Supp. is not very convincing as there can be 2 different people both with white shirts and drinking.

The process appears to capture only a portion of the video content, potentially overlooking valuable concurrent events within the video clip.

I am also curious about the visual encoders used in VideoGLaMM. Is there any specific reason or necessity for using two different image backbones separately for captioning and bounding box generation?

the reference in Line 230 is missing.

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confidence: 3
contribution: 3
initial_rating: 3
presentation: 2
questions: 1. Did the automatic annotation process take into account the ambiguities mentioned in the weaknesses section? Are there any methods in place to reject annotations for data that the model finds ambiguous?

2. Why was SAM2 not considered in the model's structural design? As far as I know, this model targets video and seems more suitable for your task.

3. I hope to see a comparative analysis between the current vLLM and video grounding models in your task, including their differences in performance, efficiency, and application scenarios. This would help in better understanding the strengths and weaknesses of each model and provide guidance for future research directions.
soundness: 2
strengths: 1. Propose a new task: grounded video caption generation, which is significant as large-scale spatio-temporal grounding of natural language is a key step in advancing fields such as human-robot interaction and embodied perception.
2. Propose a manually verified test dataset, which is likely to further drive progress in this field.
3. The model architecture is both innovative and intuitive, with subsequent ablation studies demonstrating the effectiveness of these design choices.
summary: This paper proposes a novel task—grounded video caption generation—along with a newly annotated dataset, GROC. The authors introduce a language-based tracking method to create pseudo-annotations for a large-scale instructional video dataset, named, HowToGround, which is then used to train a newly designed model. Finally, they compare the model's performance against still-image grounding baselines, reporting state-of-the-art results in grounded video caption generation.
weaknesses: 1. There are concerns regarding the technical soundness of the AUTOMATIC ANNOTATION METHOD section. I understand that the grounded video caption generation task requires high precision; however, due to the richness and ambiguity of language, I am skeptical about the consistency of the language-based tracking method in object localization. For example, while the case of the woman drinking a beverage in the authors' paper is unambiguous, many instances will likely present significant ambiguity. If, in the third frame of Figure 2, the image caption still states 'holding a glass,' how can one determine whether 'beverage' and 'glass' refer to the same object?

2. The writing quality is poor, containing a lot of redundancy, and some phrases read awkwardly and lack fluency. For instance, the phrase 'Differently than the image-based task, where bounding boxes...' could be improved. Additionally, some sentence structures need adjustment, and paragraph transitions require attention, particularly when describing the GROC construction process. There are also typos to address, such as in Section ?? on page 5. Overall, the writing and illustrations need further polishing.