Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos

## Abstract

Abstract A short clip of video may contain progression of multiple events and an interesting story line. A human need to capture both the event in every shot and associate them together to understand the story behind it. In this work, we present a new multi-shot video understanding benchmark Shot2Story20K with detailed shot-level captions and comprehensive video summaries. To facilitate better semantic understanding of videos, we provide captions for both visual signals and human narrations. We design several distinct tasks including single-shot video and narration captioning, multi-shot video summarization, and video retrieval with shot descriptions. Preliminary experiments show some challenges to generate a long and comprehensive video summary. Nevertheless, the generated imperfect summaries can already significantly boost the performance of existing video understanding tasks such as video question-answering, promoting an under-explored setting of video understanding with detailed summaries.

## 1 Introduction

Figure: Figure 2: Statistics of Shot2Story20K . Our dataset comprises videos with 2 to 8 shots each. Most shots range from 1 to 5 seconds, accompanied by detailed visual captions and narration captions. It features extensive summaries, highlighting video progressions, transitions, camera cuts and narration descriptions, with statistics of frequent expressions depicted in the figure.
Refer to caption: x2.png

Video captioning is a long-standing video understanding task to facilitate open-world video analysis with the help of human-annotated captions. Since a video may contains multiple events, dense captioning benchmarks (Ego4D [], YouCook2 [], ActivityNetCaps []) are tailored to capture the information of multiple events in a video ranging from 3-20 minutes. However, even within seconds, we find that there are already more than one single event in a lot of daily videos such as news broadcast, tutorial videos, and movies. Specifically, shot transition, which is a common technique to transfer from one event to another, or to switch the viewpoint of a single event, happens less than every 4s for average English movies after 2010 []. Although some existing captioning benchmarks [, , ] already use multi-shot videos, they often annotate the captions in a coarse-grained manner, either providing a holistic caption or asking annotators to subjectively choose the boundary of each event. To better accommodate the multi-shot formation of videos, we believe a new video benchmark with rich textual descriptions based on video shots is favored in the research community.

On the other hand, multi-shot videos are often accompanied by rich narrations that relates to the different events happening in the video. A model needs to capture both the visual and audio signals to understand the underlying story. Specifically, narrations may contain key information that cannot be inferred from pure visual information only. See Figure [1](#S0.F1), without the narration, a viewer is unable to capture the relationship between the man’s action and the avocado product in the first shot.

In this work, we propose a new benchmark Shot2Story20K for audio-visual understanding of multi-shot videos. We collected a dataset of 20,023 short videos where the average number of shots in each video is $4$. For each video shot, we annotate a detailed textual description for the video frames and another textual description for the human speech. We also leverage a state-of-the-art large language model (LLM) GPT-4 [] to generate a long textual video summary from the annotated clip descriptions, which are further verified by human annotators. The summary includes additional details such as transitions of different shots, progression of multiple events, and mapping of the subject identities in different scenes. An overview of our dataset can be seen in Figure [2](#S1.F2).

To benchmark the advances of multi-modal video understanding, we designed several distinctive tasks using our dataset, including single-shot captioning, multi-shot summarization, and video retrieval with shot description. We design and implemented several baseline models using a frozen vision encoder and an LLM, by prompting the LLM with frame tokens and ASR (Automatic Speech Recognition) text. Through extensive experiments, we show that: (1) the ASR text is critical to understand the complex multi-shot scenario, (2) processing the video as a whole without the shot-structure degenerates the model’s capacity of understanding the multi-shot video, (3) the summarization model trained on our benchmark can be generalized to other datasets with longer durations (ActivityNet) and out-of-domain topics (MSRVTT).
Without any bells and whistles, we attains competitive results on zero-shot video question-answering by converting the problem into pure text-based QA with the generated video summaries.

## 2 The Shot2Story20K benchmark

### 2.1 Overview

Our new benchmark Shot2Story20K contains 20,023 videos. The length of each each video is ranging from 10s to 40s. For each video, we first use a off-the-shelf shot detection method TransNetV2 [] to split it into shots. For each video shot, we annotate captions for both visual and audio information. Then we further annotate video summaries based on the annotated shot captions. Figure [2](#S1.F2) shows an overview of our dataset with some key statistics. An example of one annotated video is shown in Figure [1](#S0.F1).

**Table 1: High level comparison of our dataset to previous ones. The summary length of ActivityNet and YouCook2 are their combined length of captions in one video. M and G stands for manual and generated, respectively.**
| Dataset | Annotation | Multi-shot<br>Video | Multi-event<br>Descriptions | Audio<br>Captions | Detailed<br>Summary | Summary<br>Length | #Videos | Avg.<br>Duration |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| MSRVTT [] | M | ✓ | ✗ | ✗ | ✗ | - | 10K | 15s |
| ActivityNet Caps [] | M | ✓ | ✓ | ✗ | ✗ | 52.4 | 20K | 3min |
| VideoStorytelling [] | M | ✓ | ✓ | ✗ | ✓ | 162.6 | 105 | 12.5min |
| Ego4D [] | M | ✗ | ✓ | ✗ | ✗ | - | 10K | 23min |
| YouCook2 [] | M | ✓ | ✓ | ✗ | ✗ | 67.8 | 2K | 6min |
| VAST [] | G | ✓ | ✗ | ✓ | ✓ | 32.4 | 27M | 5$\sim$30s |
| Shot2Story20K | M+G | ✓ | ✓ | ✓ | ✓ | 201.8 | 20K | 16.7s |

### 2.2 Data preparation

We source videos for our dataset from the public video benchmark HDvila100M []. It offers a large collection of narrative videos, comprising 3M YouTube videos segmented into 100M clips, each about 13 seconds long.
We choose this data source for its concise yet complex multi-shot formats, diverse topics, and abundant ASR content. Since we prefer videos with both rich visual and ASR information, we design several filtering techniques to exclude those videos with either low visual-ASR correlation or static visual content.

We start with keeping video clips with durations between 10 to 40 seconds, since we observe that the majority of the video clips from HDvila100M fall in this range.
Then we remove videos with more than 8 shots due to the heavy annotation cost. We also notice that the video segments with too many shots in HDvila100M tend to be slideshows or image collages that deviates from our focuses.
Further, to harvest videos with rich visual-ASR correlations, we set up a metric between video shots and ASR texts. Specifically, we uniformly sample 4 video frames for each shot and obtain the cosine similarity score between the video shot embedding and the text embedding using CLIP [] encoders. We only keep the videos containing at least one shot that is visually correlated to ASR with a threshold of 0.25.
In the next step, in order to obtain videos with diverse shot contents, we set up an inter-shot metric to filter out the videos with similar adjacent shots. We compute the cosine similarities between embeddings of adjacent shots and keep the videos with all inter-shot similarity scores smaller than 0.9.
Finally, to further remove the videos with static contents, we adopt an intensity-based scene changes filter in PySceneDetect(^1^11https://www.scenedetect.com/)
with a threshold 11 in our segmented shots. If the filter is unable to detect new segments at a low threshold, it is conceivable that the shot contains static contents. We only keep the video clips in which all shots contain no static content based on our filtering method.

As a result, from a total of 1.1M sampled video clips from HDvila100M, we obtain $20,023$ video clips that meet our quality standard. The number of shots in each video is from 2 to 8.
These videos are then shared with our annotators for further annotations.

### 2.3 Annotation of single-shot captions

After using TransNetV2 to divide the target videos into video shots, we ask annotators to
annotate both visual-only captions and audio-related captions for each shot.
We split the annotation of these two captions to facilitate separate modeling of these two types of information source.

For visual-only caption, we require annotators to describe the major subjects and events in the video. Since it is an open-world setting, the videos can be quite diverse and hard to describe. In order to reduce the difficulties of annotating a caption from scratch, we generate an initial video caption using MiniGPT-4 [] by sampling 4 image frames from the video clip and prompting the model using the prompt below.

*MiniGPT-4 prompt: ###Human:<Img>Frame1</Img ><Img>Frame2</Img><Img>Frame3</Img><Img>
Frame4</Img>Please describe this video. Do not include details that you are not sure of. For example, if there is text in the image, do not include the content of the text if they are not clearly shown. ###Assistant: *

Although MiniGPT-4 is originally designed for image understanding, empirically it is able to generate captions for videos, both comprehensively and reasonably. It is able to describe different subjects including person, animals, food, tools, and virtual objects like animated characters. We ask annotators to correct any mistakes they find in the generated captions, and to add missing details to the captions. The mistakes include incorrect description of the object categories, attributes, actions, facial expressions etc. Also, there might be some subjective description generated by MiniGPT-4 such as emotion and atmosphere. We ask annotators to remove all these subjective descriptions. An example of this annotation process is shown in Supp. Sec. [5.1](#S5.SS1), where the annotator corrects the caption from “standing in front of the car” to “getting close to the car”, and adding a missing detail of “a close-up shot of the front”. In this way, we find the annotation speed significantly faster ($\small\sim$ $3\times$) compared to writing a caption from scratch. On the other hand, we find the captions generated this way has more coherent style and tend to cover more details of the video.

In contrast to the traditional video captioning benchmarks [, , ], we also annotate narration captions in addition to the visual-only captions. Different from existing audio captioning benchmarks [], we focus more on human speeches rather than acoustic events. The annotators are required to associate the human speech with the video content and summarize the main idea of the speech. We require annotators to describe the source of the speech using visual information. For example, if someone is talking, the annotator needs to describe which person in the video is talking. If the human speech refers to some object in the video, the annotator is required to describe which object in the video the speaker is referring to. Note that the speaker identity and reference of visual objects are critical information for understanding a video that cannot be trivially obtained using existing algorithms. There are existing research on speaker identification [] and visual grounding [, ], but they only work well on constraint scenarios. Given this annotation process, our narration captioning task requires a joint understanding of visual and audio signals.

### 2.4 Annotation of video summary

To create video summaries with the annotated video-shot captions, we leverage an LLM-based approach. Specifically, we form a text prompt with incorporating all shot captions and ASR text included, and uses GPT-4 [] to generate a cohesive summary. The text prompt we use is shown in Supp. Sec. [5.2](#S5.SS2).
The quality is assured through further review and correction by our annotators.

We prompt GPT-4 to produce coherent, fluent text summaries with transition expressions such as “the video begins”, “following this”, and “in the final scene” to connect video-shot descriptions. The generated annotations also encompass a higher-level understanding of shots, using key phrases such as “scene shifts back to” and “returns to the scene” to denote recurring scenes across different shots. Notably, GPT-4 often identifies and links the same subjects across scenes without relying on explicit re-identification models. It draws on descriptive and attributive text from our shot captions like “a newsroom” or “a man wearing a black suit” to infer scene or subject identity. To ensure quality, annotators carefully review and correct any inconsistencies in scene or subject references within these summaries.

Figure: Figure 3: Model structure for video-shot captioning. Visual tokens from the CLIP [] visual backbone and Q-Former [, ], along with text prompts, form the input to the LLM []. ASR input is optional for single-shot video captioning.
Refer to caption: x3.png

### 2.5 Comparison to existing benchmarks

Compared to existing video description datasets, our dataset is more challenging due to the explicit modeling of the multi-shot nature of web videos. Our textual description includes both shot-level captions and video-level summaries, combining visual and audio understanding, which provides a unique test bed for multi-modal video understanding. Table [1](#S2.T1) shows a high-level comparison of our new dataset with existing video captioning benchmarks. Most existing video captioning benchmarks, such as MSRVTT [], YouCook2 [] and ActivityNet Caps [], also use multi-shot videos as annotation source, but they either annotate a holistic caption for the video (MSRVTT) or ask annotators to decide the boundary of different events. In our study, we observe that video shots naturally create a sequence of related events, leading us to annotate distinct captions for each shot. Ego4D [] only annotates dense visual captions but not audio captions for relatively long egocentric videos. Video Storytelling [] is a small-scale dataset with annotations of multiple events in a videos and provides a summary of the video by concatenating all captions.

A recent work VAST [] feed generated video and audio captions into an LLM to generate video summary. However, their work processes a multi-shot video as a whole and lacks the granularity of the events in different shots. Additionally, VAST directly uses predicted captions without any human verification, which indicates their video summaries can be noisy and containing biases from the captioning models.
Our dataset stands out from VAST with its more detailed visual and audio shot captions. These captions, averaging 35.3 words for visual and 17.8 words for audio, are the result of a thorough manual annotation process.
Although our video summary is also generated using an LLM, it is further verified by annotators to make sure there is no hallucinated details from the LLM. Our dataset has an average length of $201.8$ wprds for the video summary, which is much longer than existing benchmarks, and longer than the combined length of captions in one video in ActivityNet and YouCook2.

## 3 Tasks and Experiments

### 3.1 Basic settings

For all the tasks described in this section, we follow the same training/validation/test split. Specifically, the number of videos for training, validation, and test set are 14016, 1982 and 4025, respectively.
We resize the frames to $224\times 224$.
We employ ViT-G/14 from EVA-CLIP [] and Q-Former from BLIP-2 [] as visual encoder, and Vicuna v0-7B [] as the language model. We load pretrained Q-Former from MiniGPT-4 [].
In training, we update only Q-Former parameters, keeping the ViT and LLM frozen. We adopt AdamW [] as our optimizer and use a learning rate of 8e-5. We train the models for 40 epochs with a batch size of 128 for single-shot video captioning and narration captioning.
We finetune our video summarization models on the single-shot captioning model with a batch size of 16.

### 3.2 Single-shot video captioning

**Table 2: Performance of single-shot video captioning task. V and A means Visual and ASR.**
| Modalities | B | M | R | C |
| --- | --- | --- | --- | --- |
| V | 10.5 | 16.0 | 30.1 | 38.8 |
| V+A | 10.7 | 16.2 | 29.6 | 37.4 |

To understand the visual content of each video shot, we introduce the single-shot video captioning task. Note that the task is to generate descriptions for individual video shots, while ASR information can be leveraged to improve the accuracy of the captions. For this task, we adapt the framework of MiniGPT-4 [], with the model structure depicted in Figure [3](#S2.F3). Specifically, we adopt the similar structure as we generate pseudo captions for data annotation in Sec. [2.3](#S2.SS3). First, we sample $N_{s}$ frames from a video shot, and encode them using a fixed vision encoder, then feed the encoded features to a Q-Former to produce visual tokens. The visual tokens are appended into a text prompt and the LLM is asked to generate a caption for this video shot.

Figure: Figure 4: Model structure for multi-shot video summarization model SUM-shot. We arranges visual tokens in a multi-shot format to encapsulate multi-shot information. Additionally, ASR text is incorporated for audio-visual video summarization.
Refer to caption: x4.png

We compare two model variants on this task. One is with the ASR text as additional context cues in the text prompt and the other is without the ASR information. We evaluate our models using BLEU@4 [] (abbreviated to
B), METEOR [] (abbreviated to M), ROUGE [] (abbreviated to R) and CIDEr [] (abbreviated to C), and show the results in Table [2](#S3.T2). It shows that inclusion of ASR-derived texts yields a modest enhancement in the B and M by 0.2. Conversely, it incurs a decrement of 0.5 and 1.4 in R and C, respectively. These results imply that ASR text complements visual data without introducing discrepancies, yet posing integration challenges for augmenting single-shot video captioning performance.
Figure [5](#S3.F5) (a) displays output examples of our model’s single-shot video captioning. It accurately details visual elements within the shot, effectively capturing actions like “gesturing with her hands” and articulates secondary elements within a scene like “a doll on the couch”.

**Table 3: Performance of single-shot narration captioning task. V and A means visual and ASR.**
| Method | Modalities | B | M | R | C |
| --- | --- | --- | --- | --- | --- |
| VALOR [] | Audio | 6.6 | 10.0 | 23.9 | 13.5 |
| Ours | A | 4.7 | 17.1 | 30.3 | 130.9 |
| V+A | 18.8 | 24.8 | 39.0 | 168.7 |  |

### 3.3 Single-shot narration captioning

Human narration is another critical factor to understand a multi-shot video. It often provides information of the background knowledge and commentator’s view on visual events. We conduct experiments to predict the narration caption of a video-shot and name this task single-shot narration captioning. We adopt the same model structure as single-shot video captioning with the ASR text as additional input, except that the prediction target is the narration caption. We compare with existing audio captioning model VALOR [].
We finetune VALOR on our single-shot narration captions and show the results in Table [3](#S3.T3).
We also add another baseline model that only takes ASR text as input and predicts the narration captions using Vicuna [].

Since our narration captions contain descriptions about the related visual information as well, e.g. the subject, referred objects etc, using only ASR text does not produce satisfactory results. The baseline model VALOR is unable to capture the rich ASR text information with only the raw audio, leading to a weak performance of 13.5 in CIDEr. Our model combining visual and ASR text can generate reasonable narration captions on most cases. As shown in Figure [5](#S3.F5) (a), our model identifies narration sources and aptly describes spoken content, as highlighted by phrases like “the background voice says” and “the man in a hat is talking”.

Figure: Figure 5: Example predictions of our models. (a) demonstrates our model’s single-shot video captioning, producing precise descriptions and identifying narration speakers, e.g., gesturing with hands, a man in a hat speaking. (b) shows multi-shot video summarization, with accurate captions in green and errors in red, illustrating the model’s ability to narrate event sequences and maintain subject consistency, as seen in the progression from close-up of a backpack to transitions to a man and return to the backpack.
Refer to caption: x5.png

### 3.4 Multi-shot video summarization

Multi-shot video summarization is a new task that is distinct from existing video description tasks. It requires the model to understand the shot structure of the given video and to provide a coherently paragraph to describe the progression of events in the different shots. In this section, we experiment with three model variants. The first model SUM-text uses a two-stage approach, first generating captions using our video-shot captioning model for each video shot, then embed the generated captions into a text prompt as the input to the LLM (Vicuna-v0 []) to generate a video summary. The associated prompt can be seen in Supp. Sec. [6.2](#S6.SS2). The second model SUM-holistic uses similar model as Figure [3](#S2.F3). We uniformly sample 16 frames from the full video clip and prompt the LLM with frame tokens and ASR text. The third model SUM-shot uses a more refined framework by sampling 4 frames in each video shot and prompting the LLM with frame tokens from different shots, as is shown in Figure [4](#S3.F4). Compared to SUM-holistic and SUM-shot, SUM-text is not trained end-to-end and may loss critical information with the captioning step, for example, it cannot capture the correspondence of the same subject in two shots. SUM-holistic does not have the shot information explicitly and rely on the LLM to parse the video shots using the provided frame features. SUM-shot is given the shot structure as input, which makes it easier to generate descriptions based on the different shots.
We compare with Video-ChatGPT [] by instruction-tuning their model on our video summary data without the ASR input.

**Table 4: Performance of models on video summarization. E2E means whether the model is trained in an end-to-end approach.**
| Model | E2E | ASR | B | M | R | C |
| --- | --- | --- | --- | --- | --- | --- |
| Video-ChatGPT [] | ✓ | ✗ | 5.0 | 14.0 | 19.7 | 1.2 |
| SUM-shot w/o ASR | ✓ | ✗ | 9.8 | 18.4 | 24.9 | 4.7 |
| SUM-text | ✗ | ✓ | 12.2 | 20.4 | 27.1 | 9.2 |
| SUM-holistic | ✓ | ✓ | 10.9 | 18.3 | 26.2 | 6.3 |
| SUM-shot | ✓ | ✓ | 11.7 | 19.7 | 26.8 | 8.6 |

Table [4](#S3.T4) shows the results of the three models.
It is shown that SUM-text achieves the overall best performance, although it is a two-stage model with pre-generated shot captions. SUM-shot is slightly worse than SUM-text, indicating that better model design needs to be explored for end-to-end video summarization.
SUM-holistic is consistently worse than SUM-shot, showing the importance of the shot structure in predicting a video summary matching the transition of shots.
SUM-shot w/o ASR underperforms compared to SUM-shot and SUM-holistic, highlighting ASR’s significance in multi-shot understanding.
Video-ChatGPT is not able to match the performance of our models, potentially due to their weakness in processing multiple scenes and lack of ASR input. Video-ChatGPT directly encodes the whole video into a sequence of tokens and may loss a lot of details in the frames, while ours directly feeding frames tokens into LLM without compressing them.

Figure [5](#S3.F5) (b) illustrates our SUM-shot model’s predictive capabilities. The model adeptly narrates event sequences with appropriate emphasis. For instance, in the MacBook-example, it not only details the keyboard but also rationalizes the display of various keys, aligning with the ASR data about the touchbar discussion, thus crafting a coherent summary. Nonetheless, some predictions, marked in red, are erroneous, such as the non-existent “returns to a close-up view of the macbook” shot. These inaccuracies likely stem from the LLM’s tendency to “hallucinate” plausible yet non-factual details. Despite these errors, the model demonstrates a proficiency in generating consistent and nuanced summaries, highlighting both the potential of our model and the challenges that our dataset presents for future research.

### 3.5 Video question-answering with summary

Since the generated summaries are long and complex, the traditional captioning metrics (B, M, R, C) may not reflect the true quality of the generated summaries. We thus adopt another video understanding task, zero-shot video question-answering (QA), to further evaluate the quality of our generated summaries. Existing work [] directly uses image captions as input to an LLM to generate question response. However, no such work has been done for videos.

**Table 5: Comparison of performance for text-to-video (T2V), text-to-shot (T2S), and video-to-text (V2T) retrieval tasks.**
| Method | Pretrain Datasets | T2V | T2S | V2T |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| R@1$\uparrow$ | R@5$\uparrow$ | R@10$\uparrow$ | R@1$\uparrow$ | R@5$\uparrow$ | R@10$\uparrow$ | R@1$\uparrow$ | R@5$\uparrow$ | R@10$\uparrow$ |  |  |
| Alpro [] | WebVid-2M+CC-3M | 46.3 | 69.5 | 78.4 | 50.3 | 76.3 | 83.2 | 45.2 | 69.8 | 78.2 |
| Clip4clip [] | CLIP400M | 47.2 | 70.4 | 77.6 | 52.4 | 78.2 | 85.4 | 48.9 | 70.2 | 78.1 |
| UMT [] | CLIP400M+UMT25M | 66.3 | 81.8 | 85.8 | 68.6 | 88.4 | 92.0 | 64.9 | 82.3 | 86.2 |

Specifically, we directly apply our video summarization model on video QA benchmarks MSRVTT-QA [] and ActivityNet-QA [] by splitting the testing videos into video shots and feeding them into the SUM-shot model. The generated summaries and the associated questions are then fed into a Vicuna model to derive the answers with the prompt shown in Supp. Sec. [6.3.1](#S6.SS3.SSS1). Note there is no adaptation or finetuning conducted for the Vicuna model.
Since the original answers in the QA benchmarks are very short and the generated responses from LLM tend to be a long sentence, we levarge the gpt-3.5-turbo model to generate a binary decision of whether the answer is correct, following Video-ChatGPT []. We compare our results with Video-ChatGPT [], MovieChat [] and VideoChat [] as in Table [2](#S3.T2). Note that Video-ChatGPT and VideoChat both use large amount of instruction tuning data to learn to directly generating answers from visual features and the text prompt, while ours bypasses instruction tuning by distilling the video information into a video summary. Additionally, for a direct comparison, we evaluate Video-ChatGPT on question-answering in the same methodology as ours. As shown in Table [6](#S3.T6), our model outperforms Video-ChatGPT by a large margin. Our model also follows the zero-shot QA settings since the model only uses Shot2Story20K as training data. Note that MSRVTT contains a large portion of videos with out-of-domain topics such as tv shows and food, while ActivityNet has much longer videos than our training videos. This validates the the robustness and transferability of our model across different topics and longer videos. This surprisingly good result indicates that a comprehensive and detailed video summary is a high quality abstraction of the video, facilitating a wide range of tasks including video QA and video-based conversation. Example summaries on MSRVTT and ActivityNet can be seen in Supp. Sec. [7.3](#S7.SS3).

### 3.6 Video retrieval with shot description

Text-based video retrieval is another task to evaluate multi-modal video representations. Traditional video retrieval often utilizes highly condensed text descriptions with benchmarks such as MSRVTT [], LSMDC [], and VATEX []. Retrieval models can simplify the problem by leveraging key objects / actions in the video withoutunderstand more complex details such spatial-temporal information and user intent. We present a distinct setting for retrieval with only descriptions of one video shot. Specifically, we design three settings: (1) using a shot description as query source to query the corresponding video (T2V). (2) using a shot description as query source to query the specific shot (T2S). (3) using a video as source to query a randomly sampled shot description in this video (V2T).

**Table 6: Performance on video question answering. IT means whether the model uses video-text instruction tuning data. All methods follow the zero-shot manner.**
| Model | IT | QA | MSRVTT | ActivityNet |
| --- | --- | --- | --- | --- |
| Input | QA | QA |  |  |
| VideoChat [] | ✓ | V+T | 45.0 | 26.5 |
| Video-ChatGPT [] | ✓ | V+T | 49.3 | 35.2 |
| MovieChat [] | ✓ | V+T | 49.7 | 51.5 |
| Video-ChatGPT [] | ✓ | T | 53.7 | 37.4 |
| SUM-shot+Vicuna | ✗ | T | 56.8 | 47.4 |

We report results on the testing set of our benchmark with 4025 videos, including 15913 shots. We evaluate several baseline models including Alpro [], CLIP4clip [], and UMT [] and show the results in Table [5](#S3.T5). In the three retrieval tasks, Alpro underperforms relative to Clip4clip by approximately 2.0 points in R@1, while UMT outperforms Clip4clip significantly, with an R@1 improvement of 19.1 for T2V and 16.2 for T2S.
The performance comparison confirms that refined video-language alignment is crucial for retrieval accuracy. While Alpro employs regional token alignment and CLIP4clip uses global video-text matching, UMT advances the field with its R@1 improvements, utilizing masked modeling and distilling a ViT [] for more detailed alignment. In light of these findings, our Shot2Story20K , enriched with ASR information that closely aligns with visual elements, presents an opportunity to harness ASR as a natural linkage for improving video-text alignment, potentially guiding future enhancements in this domain.
Additionally, a comparison between video and shot retrieval tasks reveals that T2V presents a greater challenge than T2S, aligning with our hypothesis that retrieving a full video using a shot caption necessitates a more detailed understanding of the video. It confirms the capacity of our dataset to facilitate detailed and complex video understanding tasks.

## 4 Conclusion

In this work, we present a large-scale video understanding benchmark with annotations based on video shots. We provide detailed textual descriptions for each shot as well as a comprehensive video summary for the whole video. With the rich and diverse descriptions, our benchmark serves as a playground for more powerful multi-modal video understanding models, ready to be extended for a range of other video understanding tasks, such as video question answering, visual grounding, and video-based conversation.

## 5 Annotation Process

In this section, the annotation process for Shot2Story20K is detailed, including single-shot caption annotation in Section [5.1](#S5.SS1), GPT-4 summary generation prompts in Section [5.2](#S5.SS2), and human correction of summaries in Section [5.3](#S5.SS3).

### 5.1 Human annotation of video-shot captions

Our single-shot video caption annotation process, described in Section [3.2](#S3.SS2), is a two-phase approach designed for high-quality and style-consistent captions. This procedure also accelerates the annotation process by  3 times. As depicted in Figure [6](#S4.F6), the process begins with MiniGPT-4 generating initial captions from structured prompts. While these captions often correctly identify subjects such as “parking lot” and “vehicle”, they sometimes inaccurately describe actions or locations. Annotators then watch the video shot and revise these captions. For instance, errors like “standing in front of”, depicted in red, are corrected to “getting close to the car”, shown in green. Additionally, annotators enrich captions with key details, such as “a close-up shot of the front of a car.”
Our single-shot narration caption annotation process follows a similar approach. Differently, we offer ASR text and videos to the annotators and ask them to write down the visually related content and describe the source of the speech. The process has been detailed in Section [3.3](#S3.SS3).

Figure: Figure 7: Example of textual content for video in Figure [1](#S0.F1). Texts in color are specific for input video and are replaced during our generation.
Refer to caption: x7.png

### 5.2 GPT-4 summarization prompt

We utilize GPT-4 to summarize video clips, leveraging our detailed video-shot captions and ASR text. The summarization follows a prompt structure adapted from [], which defines video captions and audio captions for each shot, as depicted in Figure [8](#S5.F8). For each video, we organize shot durations, video captions, narration captions, and ASR into a text format (see Figure [7](#S5.F7)). This arranged content is then fed into GPT-4 for generating the video summary.

Figure: Figure 8: Prompt template for GPT-4 summarization.
Refer to caption: x8.png

### 5.3 Human correction of video summaries

Our detailed shot captions enable GPT-4 to effectively identify and link subjects across shots, without requiring extra re-identification modules. However, according to human evaluation, about 30% of video summaries struggle to connect objects and scenes across shots. Our annotators review these summaries alongside the video clips to correct such errors.
Figure [9](#S5.F9) illustrates this process. While GPT-4 accurately references the same location, such as “the open field” and “the same open field”, it sometimes fails to maintain continuity with elements like “the black car” across scene transitions. Annotators must watch the video and assess the initial summary to make necessary corrections for the final summary. This method ensures the production of high-quality video summaries with efficiency.

Figure: Figure 9: Human correction process of video summaries. Overlapped text is omitted for clarity. Texts in bold green represent correct content, while those in red indicate errors. Please find more explanations in Section [5.3](#S5.SS3).
Refer to caption: x9.png

## 6 Prompts Used in Our Models

In this section, we elaborate on the prompts used for training and testing our models. We detail the prompts for single-shot video captioning and narration captioning in Section [6.1](#S6.SS1). For video summarization models like SUM-shot, SUM-holistic, and SUM-text, the prompts are thoroughly explained in Section [6.2](#S6.SS2).

### 6.1 Prompt for single-shot captioning

During the training of our single-shot captioning models, we select a random text prompt for each video shot, with different model variants utilizing distinct prompts. The prompts for the single-shot video captioning model that incorporates both visual signals and ASR are depicted in Figure [10](#S6.F10), while those relying solely on visual signals are in Figure [11](#S6.F11). The prompts used for single-shot narration captioning are displayed in Figure [12](#S6.F12). In these figures, boldfaced text, such as “{asr}”, is replaced with specific video information. The arrangement of visual tokens and text prompts, as presented in Figure [3](#S2.F3), is not included here for brevity.

Figure: Figure 10: Prompt during training for single-shot video captioning.
Refer to caption: x10.png

Figure: Figure 11: Prompt during training for single-shot video caption without using ASR text.
Refer to caption: x11.png

Figure: Figure 12: Prompt during training for single-shot narration captioning.
Refer to caption: x12.png

### 6.2 Prompt for video summarization

In Section [3.4](#S3.SS4), we explore different model variants for video summarization, namely SUM-shot, SUM-holistic, and SUM-text. For training SUM-shot and SUM-holistic, we use the same text prompt as in single-shot video captioning, shown in Figure [10](#S6.F10). The key distinction between these models lies in the arrangement of visual tokens: SUM-shot incorporates shot-specific information such as shot number or index along with visual tokens from each shot, whereas SUM-holistic uniformly samples 16 frames across the video.
For SUM-text, the input prompt is similar to that of SUM-shot, with the primary difference being the attachment of single-shot captions instead of shot visual tokens. This organized prompt is then fed into Vicuana for both training and testing purposes.

### 6.3 Prompts used for zero-shot QA

In this section, we detail the prompts employed for the zero-shot video question-answering task, which is discussed in Section [3.5](#S3.SS5).

#### 6.3.1 LLM QA prompt

In our paper, we propose a unique question-answering procedure in which we generate video summaries and prompt an LLM to answer the corresponding question. The text prompt used for LLM is shown in Figure [13](#S6.F13).
To better align with the ground truth answers, we prompt the LLM to generate concise answers solely based on the provided video content.

Figure: Figure 13: Prompt used for video question answering with summaries.
Refer to caption: x13.png

#### 6.3.2 Evaluation prompt

In our paper, we follow the same evaluation procedure as outlined in [], using ChatGPT-3.5 to assess the alignment of the generated answers with the given ground truth.

## 7 Additional Experiment Results

### 7.1 Comparison of different models

In this subsection, we present an example of video summarization from our Shot2Story20K testing split in Figure [14](#S7.F14), using SUM-text, SUM-holistic, and SUM-shot. Both SUM-text and SUM-shot, with their access to shot information including shot count and visual tokens in shots, successfully capture the video’s storyline and transitions. For instance, they accurately depict the sequence involving a woman in the kitchen, almonds in an oven, and the woman speaking to the camera. SUM-text, with its two-stage training, captures more nuanced details found in single-shot captions, like “a bowl of almonds is situated on the counter.”
SUM-holistic, while effectively identifying major content and events, falls short in accurate scene sequencing due to its lack of shot-structured visual tokens. This leads to errors in narrative order, such as reversing the scenes of “shifts back to the woman in the kitchen” and “in the final scene, the woman is seen using a wooden spatula”.

Figure: Figure 14: Example for video summarization using SUM-text, SUM-holistic and SUM-shot. All three models effectively grasp the video’s main topic and content. However, SUM-shot, with its access to shot information in the input prompt, excels in structuring the storyline compared to SUM-holistic. SUM-text, developed through a two-stage training approach, captures finer details than SUM-shot, highlighting the strengths of the SUM-shot model while also indicating challenges in its training process. Please find more explanation in Section [7.1](#S7.SS1).
Refer to caption: x14.png

### 7.2 Comparison of different prompts for inference

In this subsection, we evaluate the impact of different prompts on our SUM-shot models for video summarization task, which were utilized during the training process.
Table [7](#S7.T7) reveals the SUM-shot model’s consistent performance across different prompts, with slight fluctuations ranging from 0.1 to 0.2 in B, M, and R metrics. The most notable variation occurs in C, suggesting a moderate impact of prompt choice.

**Table 7: Performance of SUM-shot model with different inference prompts. Prompt IDs correspond to those outlined in Figure [10](#S6.F10).**
| Prompt | B | M | R | C |
| --- | --- | --- | --- | --- |
| Prompt-1 | 11.7 | 19.7 | 26.8 | 8.6 |
| Prompt-2 | 11.8 | 19.7 | 27.0 | 7.5 |
| Prompt-3 | 11.8 | 19.7 | 26.9 | 7.7 |
| Prompt-4 | 11.7 | 19.6 | 26.9 | 7.8 |

### 7.3 QA summary

In this subsection, we present the results of zero-shot video question-answering using Vicuna, based on textual summaries of video samples from MSRVTT-QA [] and ActivityNet-QA []. Despite the limitations of our summarization model, which scores 8.6 in CIDEr on the Shot2Story20K test split (see Table [4](#S3.T4)), and the inherent challenges of the videos due to out-of-domain topics or extended durations, the summaries generated from our trained SUM-shot model largely succeed in capturing the key elements of the videos and providing relevant information.

MSRVTT-QA: For instance, in Figure [15](#S7.F15), video7089 from MSRVTT-QA portrays a TV show outside the scope of our Shot2Story20K . This genre typically features minimal movement within individual shots, and frequent scene transitions, but a restricted variety of scenes. Yet, our generated summary aptly identifies principal elements such as the judges and contestants, actions like “engaged in a conversation” or “picking up a guitar”, and the setting of an American Idol audition. These details equip the summary to competently address questions from MSRVTT-QA. However, some gaps in detail lead to inaccuracies: of the first 10 questions for video7089, 5 are incorrectly answered due to missing information (*e.g*., Q1, Q3), incorrect summary content (*e.g*., Q4), or misalignment with the ground truth (*e.g*., Q6, Q10).

Figure: Figure 15: Example for video question-answering using summaries - MSRVTT. We show the zero-shot video summary using our trained SUM-shot model. The summary can capture most of the video, such as the subject, the action and the transitions. We also show the answers from Vicuna to the first 10 questions and the evaluation results from GPT. Please find more explanations in Section [7.3](#S7.SS3).
Refer to caption: x15.png

ActivityNet-QA: In Figure [16](#S7.F16), we present the video v_mZYWfmsYQPA from ActivityNet-QA. The video’s duration is 104 seconds, which is considerably longer than the average in our Shot2Story20K . Our summary effectively identifies important elements such as the main subject’s clothing described as “dressed in a black shirt”, the actions including “speaks to the camera” and “playing table tennis”, and the setting, noted as “in a gym”. These comprehensive details enable the Vicuna model to correctly answer most questions from ActivityNet-QA. Despite these insights, some omissions and discrepancies in the summary contribute to inaccuracies in answering. Specifically, three out of ten questions are answered incorrectly due to either a lack of specific details or mismatches with the ground truth, as seen in questions Q1, Q5, and Q7.

Figure: Figure 16: Example for video question-answering using summaries - Activity Net. We show the zero-shot video summary using our trained SUM-shot model. The summary can capture most of the video, such as the subject, the action and the transitions. We also show the answers from Vicuna and the evaluation results from GPT. Please find more explanations in Section [7.3](#S7.SS3).
Refer to caption: x16.png

## 8 Additional Implementation Details

Video-Shot Captioning. For each video shot, we uniformly sample 4 frames. For testing, a consistent text prompt is used, as depicted in Figure [3](#S2.F3). The maximum number of new tokens generated by the LLM is capped at 150 for both training and inference.

SUM-shot and SUM-holistic. In SUM-shot, 4 frames per video shot are sampled uniformly; in SUM-holistic, 16 frames per video clip are sampled. The rationale behind sampling 16 frames in a holistic approach is based on our dataset’s average of 4 shots per video, aligning with the SUM-shot approach of 4 frames per shot. For both training and inference, the LLM’s maximum new token count is set at 600. A consistent text prompt, as shown in Figure [4](#S3.F4), is used during inference.

For video-shot captioning and video summarization tasks, both models are trained on $8\times 2$ A100-80G GPUs using Pytorch. The captioning model is trained for 40 epochs, with the best-performing checkpoint on the validation set used for test performance reporting. To prevent overfitting, text prompts are randomly sampled for each sample, as detailed in Section [6](#S6).

SUM-text. For tuning the Vicuna 7B model, we arrange our text prompt (see Section [6.2](#S6.SS2)) as a question and the video summary as an answer.
During training, we use ground truth single-shot captions. During inference, we use predictions of our trained single-shot video captioning model. The model is trained on 4 A100-80G GPUs over 3 epochs with a learning rate of 2e-6, maintaining other hyperparameters at their default values from the original configuration.

Video-ChatGPT. Consistent with SUM-holistic, we uniformly sample 16 frames for both training and inference. Our prompt setup excludes ASR for video summarization, using the video summary as the answer to the question. The training is conducted over 3 epochs with a learning rate of 2e-6, and we retain other hyperparameters at their default settings as specified in the original repository.

Video Question-Answering with Summary. For the MSRVTT-QA [] and ActivityNet-QA [] datasets, we generate video summaries using our trained SUM-shot model, employing only visual tokens during inference. Upon generating these summaries, we integrate them with individual questions from their corresponding videos into the prompt format displayed in Figure [13](#S6.F13). This integrated content is then processed through Vicuna [] to obtain answers. The evaluation of these results is carried out following the methodology outlined in [].

## 9 Broader Impact

Data Limitations and Ethical Considerations. Our approach does not involve storing or releasing videos; users must download these from original sources and comply with their licenses. Given HD-VILA-100M []’s long-standing public availability, we assess a low risk of the currently available videos being removed in the near future. Additionally, our meticulous manual annotation process is designed to avoid any ethical or legal violations.

Human Rights in Annotation Process. We have conscientiously structured the annotation process to ensure fair workloads and equitable compensation for annotators, upholding human rights standards.

Scope of Conclusions. It is important to recognize that experiments and data, including ours, might only represent a subset of universal realities. Nevertheless, given the wide range of categories covered in our videos, we believe our conclusions offer a robust understanding applicable to various multi-shot video scenarios and durations. These findings, while specific to our dataset, provide significant insight into the broader field of video analysis.

Usage of Language Models. Our use of the LLaMA model [] from Meta is authorized for research purposes. Those intending to use our model post-release should ensure that they have the necessary permissions and adhere to usage restrictions. We express deep respect for the work of developers and contributors, recognizing their integral role in advancing language modelling and multi-modal learning.

Future Research and Development.
We release our code and dataset to encourage further research and enable others to build upon our work. Although our current experiments require up to 8$\times$2 A100-80G GPUs, we are aware this may be a limitation.
Consequently, we plan to focus future efforts on adapting these experiments to be compatible with a single node of 8 A100 GPUs. It’s important to note that fitting the experiments within an 8 GPU framework is not the primary focus of this paper, but we consider it a crucial step towards making our research more accessible and inclusive for a wider array of research groups.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: The paper mentions that models sometimes produce hallucinated details in the video summaries. Did you explore any methods to mitigate these issues during model training or fine-tuning? Additionally, were there specific patterns observed in these hallucinations (e.g., adding non-existent objects, exaggerating actions)?

The distinction between single-shot captioning and multi-shot summarization is well-explained. However, were there cases where summarizing at the shot level led to information redundancy or fragmentation when compiling multi-shot summaries? If so, what strategies did you use to address this issue?
soundness: 3
strengths: Shot2Story addresses the limitations of existing benchmarks by providing fine-grained shot-level annotations, narration captions, and comprehensive video summaries. This detailed annotation setup allows for a more nuanced understanding of multi-shot videos.

The dataset's scale and quality are commendable, with 42,958 videos annotated both visually and auditorily. The authors employ advanced techniques, including GPT-4 for generating initial summaries, followed by human verification, ensuring data reliability.

The authors present solid baseline models across tasks, demonstrating the benchmark's challenges and providing insights into areas where current models struggle, particularly in complex video summarization.
summary: The paper introduces Shot2Story, a benchmark aimed at improving multi-shot video understanding through a rich set of annotations, including shot-level captions, detailed video summaries, and question-answer pairs. The benchmark focuses on tasks such as single-shot captioning, multi-shot summarization, and multi-shot question-answering, each designed to leverage both visual and auditory cues in video data. The authors provide a comprehensive dataset of nearly 43,000 videos, annotated with visual and audio information at each shot level, as well as summaries and QA pairs to facilitate high-level semantic understanding of video content.
weaknesses: The reliance on automated generation may introduce inaccuracies or biases, despite human verification. Clarifying any common pitfalls in the generated summaries would strengthen the paper.

The baseline models appear to struggle with integrating visual and auditory information effectively, as noted in the single-shot captioning task. It may be beneficial to explore more advanced audio-visual fusion models as baselines.

A deeper analysis comparing Shot2Story with related benchmarks would contextualize its contributions, especially in terms of unique challenges posed by shot transitions and multi-event progression.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: I think the main concern is on its contribution of methodology, as it doesn't involve any architecture/method design for tackling the multi-shot video understanding problem.
soundness: 3
strengths: - The paper presents a new multi-shot video understanding benchmark (Shot2Story), with detailed shot-level captions, comprehensive video summaries and question-answering pairs. This will benefit the community, as a benchmark for multi-modal, multi-shot video understanding benchmark.

- The paper has made significant efforts for curating the datasets, to maintain high quality standard, getting the data from 2.1M video clips to 42,958 video clips. The descriptions of the filtering stages are clear and reasonable.

- For baseline establishment, the authors have extensively evaluated a series of open-source models.
summary: - This paper presents Shot2Story, a large-scale benchmark for comprehensive multi-shot video understanding. 

- The dataset contains detailed shot-level captions for both visual signals and human narrations, and comprehensive video summaries based on shot-level captions.

- The authors further design a challenging video question-answering benchmark for multi-shot video understanding.
weaknesses: - My main concern is on its scientific novelty, as the paper does not include new designs on tackling the multi-shot video evaluation problem, only providing a benchmark, it seems to be more suitable for a benchmark track.

- For baseline evaluation, the focus has been mainly on open-source models, what about models, for example, gpt4v, claude3.5 ?

- In Table 1, \hline is incorrect ?

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: See weaknesses.
soundness: 3
strengths: This paper addressed here underscores a critical issue in current Video LLM work, where shot-level temporal understanding in video instruction data has largely been neglected. Most existing video instruction datasets lack the fine-grained, shot-level annotations necessary to capture nuanced transitions between events within videos.

To address and evaluate this issue, this work introduces a challenging multi-shot video understanding instruction dataset and benchmark. The dataset construction process involves substantial human oversight, which significantly reduces the likelihood of inaccuracies and biases often introduced by external models (e.g., GPT).

In the experiments, the authors analyze and compare several existing Video LLM approaches, highlighting the benchmark’s applicability and challenges. Additionally, they propose two suitalbe baseline models ,which achieve considerable improvements in the understanding of multi-shot video content.
summary: For multi-shot videos, current video benchmarks tend to annotate captions in a coarse-grained manner, either by providing a holistic caption for the entire video or by allowing annotators to subjectively define the boundaries of each event. But multi-shot videos often include rich narrations that correspond to distinct events occurring within the video. Given this context, this work introduces a new benchmark, *Shot2Story*, aimed at enhancing audio-visual understanding of multi-shot videos through the detailed textual annotation of each individual shot. The authors conduct extensive experiments from three perspectives: single-shot video captioning, multi-shot video summarization, and video question-answering with video summaries. These experiments demonstrate significant limitations in current models’ abilities to understand multi-shot content. Additionally, the authors design two baseline models based on MiniGPT-4 and Video-Chat2. Experimental results reveal that the multi-shot instruction data constructed by the authors substantially improves the performance of multi-shot video understanding.
weaknesses: The methods compared across the three tasks—video shot captioning, video summarization, and multi-shot video question answering—lack consistency, making it difficult to observe a clear, uniform improvement in performance across these tasks with the proposed dataset. To improve consistency, I suggest that the authors consider using the same set of baseline models across all tasks. 

The constructed multi-shot dataset appears to resemble a series of single-shot descriptions loosely connected by simple transition words, such as "begin with," "then," and "next." The annotation process seems more akin to first splitting multi-shot videos into individual shots, generating isolated descriptions for each, and then using an external tool (e.g., GPT-4) to concatenate these descriptions. This method does not accurately represent the interconnections between shots, which may pose challenges in teaching large language models (LLMs) multi-shot temporal reasoning skills using the constructed instructional data. Although the actual content contained in different shots is inconsistent, there is an obvious time sequence and event relationship between them. I suggest that the authors improve the annotation process by having annotators explicitly describe relationships or continuity between shots, rather than relying on simple transition words. This approach would better capture the temporal flow and coherence between shots, which is essential for developing multi-shot temporal reasoning capability in LLMs.


Moreover, it raises the question of whether multi-shot understanding is at odds with simple video understanding and long-video comprehension. I noticed that the average length of videos in the dataset is only 17.1 seconds, and shots with minimal variation between them were deliberately excluded during construction. Although the videos are relatively short, they contain numerous individual shots with significant differences between them. This could lead models to develop a bias toward segmenting any input video into multiple shots for interpretation, which may pose challenges for understanding both simpler and longer videos. I recommend that the authors conduct additional experiments to evaluate whether models trained on this dataset struggle with simple single-shot videos or longer videos, as this could help clarify any potential biases introduced by the dataset construction.

---

confidence: 2
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Is it an optimal approach to use large language models (LLMs) to annotate datasets and then use those annotations for reasoning tasks by the LLMs themselves? This self-referential process might introduce biases or limitations in understanding. Is there a better approach to improving the depth of reasoning and multi-modal alignment, perhaps involving a more collaborative method of training distinct specialized models (e.g., one focused on annotation and another on reasoning)?
soundness: 3
strengths: 1. The paper is well-written, providing clear explanations of the benchmark, tasks, and dataset construction, making complex ideas accessible.
2. Shot2Story sets a new standard for multi-shot video understanding, with detailed shot-level captions, audio narrations, and summaries that advance research in video analysis and multi-modal understanding.
3. The benchmark's unique focus on multi-shot videos with rich shot-level annotations differentiates it from previous benchmarks, contributing to a better understanding of event transitions in multi-shot videos.
4. The authors conduct thorough experiments using baseline models like MiniGPT-4 and VideoChat2, providing valuable insights into current models' strengths and limitations, and establishing a solid foundation for benchmarking future models.
summary: The paper presents a new benchmark for understanding multi-shot videos, named Shot2Story. The benchmark provides a dataset of 42,958 short videos, each consisting of an average of 4.4 shots, with detailed textual descriptions, video summaries, and question-answering pairs for multi-modal understanding. It aims to enhance the understanding of videos by providing shot-level captions, human narrations, and generated summaries. Several distinct tasks are defined using this dataset: single-shot video captioning, multi-shot video summarization, and multi-shot video question answering. The paper also presents baseline models and demonstrates the challenges of long, comprehensive video summaries.
weaknesses: 1. There is a format issue in Table 1 that affects its readability. Consistent formatting is essential for clarity and for the accurate presentation of comparative data.
2. The motivation for annotating audio text descriptions is not clearly articulated. Understanding audio alongside video is crucial for comprehensive multimedia understanding, especially in tasks involving multi-modal large language models (MLLMs). Merely using textual descriptions without integrating auditory context reduces the potential for deep reasoning capabilities across modalities, which limits the advancement of MLLM applications.
3. The experimental section lacks baselines from well-regarded models such as the Qwen-VL series. Including such comparisons would provide a stronger benchmark and enable a more effective evaluation of the proposed model’s performance.
4. The paper does not propose any self-developed model tailored specifically for the Shot2Story benchmark, which would demonstrate the specific advantages and limitations of the benchmark through a purpose-built approach.

Meta-review: This paper introduces a multi-shot video understanding benchmark, providing captions for both visual signals and human narration. Based on this new benchmark, several tasks are evaluated to establish a foundation for future research.
Initially, the submission received mixed feedback. While reviewers acknowledged the strengths of the proposed benchmark, two reviewers leaned toward rejection. The first reviewer (G1Gp) raised the following concerns: (1) the submission might be more appropriate for the dataset track due to a lack of technical novelty, and (2) it did not include comparisons with non-open-source models such as GPT-4V and Claude 3.5. In the rebuttal, the authors clarified that the submission's primary area is indeed aligned with ICLR’s Datasets and Benchmarks track. This clarification prompted the reviewer to raise their rating to a 6.

The second opposing reviewer (5bm5) did not respond to the rebuttal, despite the AC making repeated attempts to bug them. The AC reviewed the rebuttal and identified that the reviewer had highlighted three weaknesses: (1) the need for clarification regarding pitfalls in the generated summaries, (2) differences and challenges compared to related datasets, and (3) the need for a more advanced baseline for audio-visual fusion. The rebuttal appeared to address the first two clarification issues. 

Although the third point about advanced baselines remains, the AC thinks that the strengths of the submission outweigh its weaknesses, particularly considering its contributions to the dataset track. Consequently, the AC recommends accepting the submission.

Decision: Accept (Poster)