Shot2Story

A New Benchmark for Comprehensive Understanding of Multi-shot Videos

*Equal contribution
1ReLER, AAII, University of Technology Sydney, 2Bytedance Inc., 3Data61, CSIRO, 4Department of Computer Vision, MBZUAI
🌟 Data instruction: Please find our instructions for using and downloading data here.
🚀 Demo Release: SUM-shot model
Detailed and grounded video summaries are powerful! Please check our demo for SUM-shot model. Chat-SUM-shot model is on the way!
🚀 Latest Release: Video Summarization & Shot Captioning Code
Dive into our codes for video summarization and shot captioning, enhancing visual-audio video analysis. Stay tuned for more tasks and codes coming soon!
🌟 New Release: 20K version of Shot2Story
Explore 20k videos with detailed human-annotated summaries; 80k video shots with visual captions, and 40k shots with narration captions for comprehensive audio-visual analysis.

In light of large models, we introduce the Shot2Story benchmark: a multi-shot video resource with detailed textual annotations, ideal for training and validating diverse, temporally distinct video tasks. Check the chatbot powered by Shot2Story.

Abstract

A short clip of video may contain progression of multiple events and an interesting story line. A human needs 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 Shot2Story 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 underexplored setting of video understanding with detailed summaries.




Shot2Story


We provide 20k videos with diverse topics and contents. Each video is annotated with shot-level captions and comprehensive video summaries. To facilitate better semantic understanding of videos, we provide captions for both visual signals and human narrations.


Dataset glance

The dataset includes an average of 4.0 shots per video, resulting in a total of 80k video shots, each with detailed video caption and narration caption annotations. The average length of our video summaries is 201.8, while the average length of a video is 16s.

For more comprehensive details, please refer to the plots below.


Comparison to datasets

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.


Baselines and Tasks

We experiment on tasks like single-shot video captioning, narration captioning, video summarization, video shot retrieval and video question-answering. Papers and codes are on the way. Please check for updates!


Single-shot captioning - visual and audio

For each single video shot, we experiment on single-shot video captioning and narration captioning. Model structure is shown below. Visual tokens from the CLIP visual backbone and Q-Former (together with a linear layer from MiniGPT4), along with text prompts, form the input to the Vicuna. ASR input is optional for single-shot video captioning.

For video captioniong, we try visual singals and visual-audio signals as input. See the results below.

For narration captioniong, we show the results of ASR texts only and our visual-audio model below, which shows the importance of incorporating visual signals.


Video summarization - visual and audio

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.

We propose SUM-shot model, as a powerful baseline for multi-shot video analysis. We sample 4 frames in each video shot and prompting the LLM with frame tokens from different shots, as shown below.

We experiment with different models, such as the Video-ChatGPT, SUM-shot holistic (which doesn't have shot information), SUM-text (two-stage approach with LLM tuned) and SUM-shot w/o ASR (which doesn't ASR text as input). Experiment shows that shot information and ASR information are crucial to multi-shot video summarization. Moreover, the task poses a challenge for better training scheme, as indicated by superior performance of SUM-text.


Zero-shot video question-answering

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. 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.

As shown in the above table, 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.

BibTeX


        @article{han2023shot2story20k,
          title={Shot2Story20K: A New Benchmark for Comprehensive Understanding of Multi-shot Videos}, 
          author={Mingfei Han and Linjie Yang and Xiaojun Chang and Heng Wang},
          journal={arXiv preprint arXiv:2311.17043},
          year={2023}
        }