Source-linked AI summary
VideoChat: Chat-Centric Video Understanding
KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, Yu Qiao
TL;DR
VideoChat addresses the limited generality of task-specific video understanding and the information loss of textual video representations. It integrates video foundation models with LLMs through a learnable neural interface and trains them with a video-centric instruction dataset, with preliminary qualitative experiments demonstrating potential across video applications.
Problem
Task-specific video tuning and textualization limit general spatiotemporal comprehension, while existing vision models struggle with event localization and causal inference.
Method
VideoChat combines video foundation models and LLMs through a learnable neural interface, using lightweight alignment training and a video-centric instruction dataset.
Results
Preliminary qualitative experiments demonstrate VideoChat’s potential for spatiotemporal reasoning, event localization, causal inference, and broad video applications.
Takeaways & Limitations
VideoChat provides a prototype for chat-centric video understanding and a training resource emphasizing spatiotemporal reasoning and causal relationships.
Takeaways & Limitations
Both VideoChat versions struggle with long-term videos of at least one minute, and temporal and causal reasoning remain rudimentary.
Abstract
from arXiv · showhide
In this paper, we initiate an attempt of developing an end-to-end chat-centric video understanding system, coined as VideoChat. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference. To instructively tune this system, we build a video-centric instruction dataset, composed of thousands of videos associated with detailed descriptions and conversations. This dataset emphasizes spatiotemporal reasoning and captures causal relationships, providing a valuable asset for training our chat-centric video understanding system. Preliminary qualitative experiments demonstrate the potential of our system across a broad spectrum of video applications, which could serve as a simple prototype system for future research on chat-centric video understanding. Access our code and data at https://github.com/OpenGVLab/Ask-Anything
1 Introduction
VideoChat addresses limits in task-specific video understanding by integrating video and language foundation models through a learnable interface and introducing a video-centric instruction dataset. The system targets richer spatiotemporal reasoning, causal inference, and conversational video understanding.
- Current video-understanding paradigms rely on task-specific tuning, limiting general spatiotemporal comprehension for real-world video applications.Existing vision-centric dialogue systems also have not formally addressed video-centric tasks from a data-centric perspective.
- Textualizing videos loses visual information and oversimplifies spatiotemporal complexity, while vision models struggle with event localization and causal inference.
- VideoChat integrates video foundation models and LLMs through a learnable neural interface, forming a fully learnable video understanding system.The system combines model-side integration with data-side techniques for learning the system.
- Two-stage lightweight training uses spatiotemporal and video-language alignment modules with large-scale video-text and self-built instruction datasets.The authors state that this training supports spatiotemporal perception, reasoning, and causal inference.
- The instruction dataset contains thousands of videos paired with detailed descriptions and conversations emphasizing spatiotemporal objects, actions, events, and causal relationships.
- The work presents chat-centric video understanding as a prototype for integrating video and natural language processing across video applications.
2 Related Work
Related work establishes video-language modeling, large language models, and multimodal dialogue as the foundations for VideoChat. The framework represents videos either as textual streams or embeddings before supplying them to an LLM for multimodal understanding.
- Video Foundation Models: Video-language systems commonly pretrain on large-scale video-text data and fine-tune downstream tasks, with newer approaches favoring end-to-end training.
- Large Language Models: Large language models provide language generation and in-context learning, enabling complex tasks from user prompts in zero-shot settings.
- LLMs for Multimodal Understanding: Multimodal systems combine vision models with LLMs for visual reasoning, dialogue, explanation, question answering, and related tasks.
- VideoChat Framework: VideoChat-Text textualizes videos into streams, whereas VideoChat-Embed encodes videos as embeddings; both provide video content to LLMs.
3 VideoChat
VideoChat formulates video understanding as multi-round question answering, using either textualized video descriptions or learned video embeddings to support language-model reasoning. Its systems combine video and language models through structured prompting or a learnable interface, with two-stage training and video instruction data aimed at temporal and causal understanding.
- VideoChat formulation: VideoChat treats an LLM as a universal video-task decoder for multi-round video question answering, interpreting video descriptions or embeddings into human-readable responses.Tasks are specified through user words during inference, with no or few learning instances.
- VideoChat-Text: VideoChat-Text uses vision models to extract time-stamped captions, actions, objects, audio, and other video details, then organizes them into prompts for an LLM.The pipeline samples frames and audio, combines outputs from multiple perception models, and structures related content temporally.
- VideoChat-Text: VideoChat-Text communicates structured textual video knowledge to an LLM through a system prompt that restricts responses to content relevant to the video.The prompt is designed to help the LLM respond according to the document and avoid unrelated questions.
- VideoChat-Text limitations: VideoChat-Text processes 10-second clips in about 2 seconds at 1 FPS on an NVIDIA-A10 GPU, but text mediation limits perception richness and the benefits of visual instruction tuning.Using more advanced perception models could conflict with the system’s efficiency.
- VideoChat-Embed: VideoChat-Embed combines video and language foundation models with a learnable Video-Language Token Interface that compresses video tokens and aligns video representations with language.Video tokens, user queries, and dialogue context are supplied to the LLM for communication.
- VideoChat-Embed: VideoChat-Embed uses two-stage training: video-text alignment first, followed by instruction tuning on detailed descriptions and video question-answer pairs, with image data included for joint training.The instruction data is built from videos with detailed descriptions and conversations emphasizing descriptive, temporal, and causal content.
4 Experiments
Qualitative experiments present VideoChat’s video descriptions, spatial and temporal reasoning, causal inference, and comparisons with image-based dialogue systems. The examples show capabilities across descriptive narratives, recommendations, event interpretation, and video-focused conversations.
- Qualitative Analysis: VideoChat-Text generates temporally ordered video narratives using sequence adverbs and rich descriptions of observed scenes and actions.The example describes a woman at a cliff through first, next, then, and finally, while capturing atmosphere and events.
- Spatial Perception and Analysis: VideoChat-Embed recognizes objects and properties, such as clothing style and the number of people, to make visually grounded recommendations.In Figure 7, it recommends light and funny music from Japanese-style clothing, group size, and temporal movement patterns.
- Temporal Perception and Reasoning: VideoChat-Embed performs temporal reasoning by identifying actions over time, camera motion, activities, and safety-related explanations.Examples include recognizing basketball and dance movements, identifying yoga, and explaining why a practitioner is unlikely to fall.
- Causal Inference: VideoChat-Embed infers causal and abstract concepts from spatiotemporal clues, including humor, accidents, rhythm, and movement patterns.It attributes humor to erratic dancing, an accident to vehicle collision and damage, and music suitability to dancing rhythm and progression.
- Comparisons: In a TVQA comparison, VideoChat-Embed correctly identifies the scene where image-based systems misperceive the conversation setting as indoors.The comparison uses an online-demo query example and reports consistent proficiency on a related video.
5 Conclusion
The conclusion presents VideoChat as an initial multimodal dialogue system for general video understanding, combining language models with video foundation models and instruction data. Initial qualitative evaluations indicate promising capabilities, while long videos and advanced temporal and causal reasoning remain limitations.
- Conclusion: VideoChat combines video foundation models and large language models through a trainable neural interface for end-to-end video understanding.The paper implements text-based and end-to-end versions and frames the system as a preliminary video-to-text approach.
- Conclusion: The video-centric instructional dataset emphasizes spatiotemporal reasoning and causality for video-based multimodal dialogue systems.The dataset is presented as a learning resource supporting the system’s development.
- Conclusion: Initial qualitative evaluations show promising capabilities across various video applications.The conclusion describes these evaluations as supporting ongoing advancement of the system.
- Limitations: VideoChat-Text and VideoChat-Embed struggle with videos lasting ≥1min, while temporal and causal reasoning remain rudimentary.The paper attributes these limitations to instruction-data scale and construction, system scale, and the employed models.
- Future Works: Future work targets larger video foundation models, scalable video-centric training data and benchmarks, and long-term video processing.These directions are stated as the paper’s future-work priorities.
A Appendix
The appendix lists instructions for brief and detailed image and video descriptions. Image instructions are inherited from LLaVA, while video instructions are generated by ChatGPT using image-list examples.
- Appendix: The appendix includes instruction lists for brief image and video descriptions.The image list is inherited from LLaVA, and the video list is generated by ChatGPT with image-list examples.
- Appendix: It also includes instruction lists for detailed image and video descriptions.The same provenance distinction is given for the detailed-description lists.