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Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
TL;DR
Video-ChatGPT addresses the limited ability of existing video-understanding models to sustain coherent open-ended conversation. It combines a video-adapted visual encoder with an LLM, trains on 100,000 video-instruction pairs, and introduces quantitative video-conversation evaluation. The model performs well across key evaluation aspects relative to contemporary video conversation models, while remaining challenged by subtle temporal relationships in long videos and small-object details.
Problem
Existing video understanding models were unable to hold coherent open-ended conversations about video content, despite progress on video-related tasks.
Method
Video-ChatGPT adapts LLaVA by combining a CLIP visual encoder with an LLM, modifying visual processing for spatiotemporal video features and fine-tuning on video-instruction data.
Results
Video-ChatGPT performs well across key quantitative evaluation aspects compared with contemporary video conversation models.
Takeaways & Limitations
The paper contributes 100,000 video-instruction pairs and a quantitative framework for evaluating video conversation capabilities.
Takeaways & Limitations
Video-ChatGPT struggles with subtle temporal relationships in videos longer than two minutes and often misses details of small objects.
Abstract
from arXiv · showhide
Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data. While there have been initial attempts for image-based conversation models, this work addresses the under-explored field of \emph{video-based conversation} by introducing Video-ChatGPT. It is a multimodal model that merges a video-adapted visual encoder with an LLM. The resulting model is capable of understanding and generating detailed conversations about videos. We introduce a new dataset of 100,000 video-instruction pairs used to train Video-ChatGPT acquired via manual and semi-automated pipeline that is easily scalable and robust to label noise. We also develop a quantitative evaluation framework for video-based dialogue models to objectively analyze the strengths and weaknesses of video-based dialogue models. Code: https://github.com/mbzuai-oryx/Video-ChatGPT.
1 Introduction
Video-ChatGPT addresses the lack of coherent open-ended video conversation by combining an adapted visual encoder with an LLM. The work contributes scalable video-instruction data and quantitative benchmarking for video dialogue models.
- Video understanding models had advanced on video tasks but remained unable to sustain coherent open-ended conversations about video content.The paper motivates video dialogue as a unified interface for tasks including recognition, localization, detection, segmentation, retrieval, and tracking.
- The paper introduces the first quantitative framework for benchmarking video conversation models across correctness, detail, context, temporal understanding, and consistency.The framework is intended to support more accurate evaluation of video conversation performance.
- Video-ChatGPT merges a pretrained visual encoder with an LLM to model spatiotemporal video representations and generate meaningful video conversations.Its design adapts image-based multimodal modeling to video inputs while retaining temporal and spatial characteristics.
- 100,000 high-quality video-instruction pairs were created with a scalable annotation framework producing diverse video-specific instruction sets.The dataset combines human-assisted and semi-automatic annotation methods.
- Video-ChatGPT performs well compared with concurrent video conversational engines, including Video Chat.The comparison is presented as evidence from the paper’s quantitative evaluation framework.
2 Related Works
Prior multimodal systems combined pretrained image models with LLMs, while related video systems integrated video or image foundations with language models. Video-ChatGPT differentiates itself through scalable high-quality instruction annotation, spatiotemporal CLIP features, and quantitative video-conversation evaluation.
- Vision-language research increasingly combined pretrained visual encoders with LLMs, but many multimodal systems did not consider video inputs.The paper positions video modeling as requiring preservation of temporal and spatial characteristics alongside language generation.
- LLaVA and InstructBLIP demonstrated image-based multimodal instruction tuning and dialogue capabilities using LLMs with visual encoders.LLaVA used generated multimodal instruction-following data, while InstructBLIP introduced instruction-aware visual feature extraction.
- VideoChat integrated video and image foundation models with LLMs and used a two-stage lightweight training process with video-specific generated descriptions.Its dataset relied on off-the-shelf vision-language models to generate noisy detailed textual descriptions.
- Video-ChatGPT uses human-assisted and semi-automatic annotation to generate high-quality video instruction data rather than relying only on generated descriptions.The paper presents this framework as novel, scalable, and distinct from VideoChat’s data-generation approach.
- The paper also proposes the first quantitative framework specifically for evaluating video conversation tasks.This evaluation contribution complements the model and dataset differences described relative to prior work.
3 Video-ChatGPT
Video-ChatGPT adapts an image-based vision-language model for video by extracting spatiotemporal features, projecting them into the language space, and fine-tuning on video-text instructions. The resulting system is designed to capture temporal dynamics, spatial consistency, and long-range video relationships.
- Video-ChatGPT starts from LLaVA and aligns video representations with an LLM to generate meaningful conversations about videos.LLaVA combines CLIP’s visual encoder with Vicuna’s language decoder; Video-ChatGPT adapts this foundation for video conversation.
- The adapted model is intended to capture temporal dynamics, frame-to-frame consistency, long-range relationships, and spatial, temporal, and action-oriented video components.These design goals are linked in the paper to video reasoning, creativity, and understanding.
- 3.1 Architecture: CLIP ViT-L/14 encodes video frames independently, then spatial and temporal average pooling produces complementary video representations.Spatial pooling yields temporal features across frames, while temporal pooling yields spatial features across frame-level tokens.
- 3.1 Architecture: The temporal and spatial features are concatenated and passed through a trainable linear adapter into the language decoder’s embedding space.The projected video tokens are concatenated with tokenized text queries before entering the language decoder.
- 3.2 Video Instruction Tuning: Video-ChatGPT is fine-tuned on curated video-text pairs with prompts containing an instruction and video tokens, while the pretrained video encoder and LLM remain frozen.The training uses the LLM’s original autoregressive prediction objective.
4 Video Instruction Data Generation
The paper generates video-instruction data through complementary human-assisted and semi-automatic annotation pipelines, followed by GPT-assisted conversion into diverse instructional formats. The framework enriches captions with spatial, temporal, relational, and reasoning context while filtering noise and scaling data production.
- 4 Video Instruction Data Generation: The annotation framework combines human-assisted detail with semi-automatic scalability to produce accurate, meaningful, and diverse video instruction data.The data includes descriptions, summaries, question-answer pairs, creative tasks, and conversational tasks spanning appearance, temporal relations, and reasoning.
- 4.1 Human-assisted Annotation: Human-assisted annotation enriches concise video captions with physical appearance, spatial and temporal localization, and contextual details.The process uses video-caption pairs, including a subset of ActivityNet-200, as its starting point.
- 4.2 Semi-automatic Annotation Framework: Semi-automatic annotation uses off-the-shelf dense prediction and captioning models to add contextual information to video-caption pairs cost-effectively and at scale.The approach increases data quantity without substantially compromising quality.
- 4.2 Semi-automatic Annotation Framework: BLIP-2 generates frame-level captions, GRiT supplies dense object captions, and Tag2Text produces key-frame tags for filtering irrelevant descriptions.Captions that do not match the Tag2Text vocabulary are removed to mitigate noise.
- 4.2 Semi-automatic Annotation Framework: The pipeline maintains high prediction thresholds, filters mismatched captions, and merges retained frame-level information into coherent video-level captions.GPT-3.5 is instructed to discard inconsistent information across frames while augmenting the original ground-truth caption.
- 4.3 GPT-Assisted Postprocessing: GPT-3.5 postprocessing converts enriched descriptions into varied question-answer and conversational instruction types using in-context learning.The generated formats include detailed descriptions, summarizations, question-answer pairs, creative tasks, and conversational tasks.
- 4.3 GPT-Assisted Postprocessing: The postprocessing stage combines additional contextual elements with video captions and produces final detailed descriptions for instructional data.This stage refines and optimizes enriched annotations to support accurate understanding of spatial and temporal video cues.
- 4.3 GPT-Assisted Postprocessing: The resulting data supports benchmarking across five video-conversation aspects, with Table 1 comparing 7B model variants including Video-ChatGPT and Video Chat.The benchmark evaluates correctness, detail orientation, contextual understanding, temporal understanding, and consistency.
5 Experiments
The experiments evaluate Video-ChatGPT through quantitative video-generation and zero-shot question-answer benchmarks, plus annotation and evaluator ablations. It performs competitively across datasets, achieves SOTA zero-shot VideoQA results, benefits from combined annotation sources, and shows similar trends with Vicuna-1.5 evaluation.
- Quantitative evaluation: The experiments use video-based generative performance benchmarking and zero-shot question-answer evaluation to assess Video-ChatGPT.The generative benchmark uses ActivityNet-200 videos with dense captions and human-annotated question-answer pairs; the zero-shot evaluation covers MSRVTT-QA, MSVD-QA, TGIF-QA FrameQA, and ActivityNet-QA.
- Quantitative evaluation: Video-ChatGPT shows competent performance across key aspects compared with Video Chat, LLaMA Adapter, and Video-LLaMA.The reported strengths include contextually relevant, detailed, and temporally accurate text from video input.
- Quantitative evaluation: Video-ChatGPT achieves state-of-the-art performance across MSRVTT-QA, MSVD-QA, TGIF-QA FrameQA, and ActivityNet-QA in zero-shot evaluation.The comparison includes FrozenBiLM, Video Chat, LLaMA Adapter, and Video-LLaMA, with GPT-assisted evaluation assessing generated predictions.
- Ablations: The combined human-annotated and semi-automatically annotated training dataset achieves the best performance.Human annotations comprise 30% of the data and semi-automatic annotations 70%; training only on human annotations gives the lowest overall performance because fewer labels are available.
- Ablations: Evaluation with open-source Vicuna-1.5 (13B) shows similar trends to GPT-3.5 evaluation across correctness, detail, contextual and temporal understanding, and consistency.The authors use Vicuna-1.5 because GPT-3.5 is accessed through an API and is not open-source, improving accessibility and replicability.
- Ablations: A blind test distinguishing human and semi-automatically annotated question-answer pairs achieves 52% accuracy across 50 randomly sampled videos.The authors use this result to support the reliability of the semi-automatic data and alignment between automatic evaluation and human judgment standards.
6 Conclusion
The conclusion presents Video-ChatGPT as a multimodal video-conversation model that combines pretrained vision and language components with video-instruction tuning. It also contributes a 100,000-pair dataset and a quantitative framework spanning video question answering and open-ended descriptions.
- Conclusion: Video-ChatGPT merges a pretrained visual encoder with a large language model and uses video-instruction fine-tuning for video understanding and conversation.An adapter aligns pretrained vision and language backbones while modeling temporal dynamics and spatial consistency in spatiotemporal sequences.
- Conclusion: The authors create 100,000 video-instruction pairs to enhance Video-ChatGPT’s video-specific understanding and conversation capabilities.The dataset is presented as a central contribution alongside the model.
- Conclusion: The quantitative video-conversation evaluation framework covers conventional video question answering and open-ended descriptions across diverse capabilities.The framework is introduced for benchmarking video conversation models.
7 Limitations
Video-ChatGPT has difficulty understanding subtle temporal relationships in long videos and recognizing details of small objects. These limitations can compromise predictive performance and omit information embedded in fine details.
- Subtle temporal relationships in videos longer than 2 minutes are challenging to understand.This difficulty can compromise predictive performance.
- The model has difficulty recognizing details of small objects.It may consequently miss additional information embedded in those details.
- Long-video temporal reasoning and small-object recognition remain concrete performance boundaries.Both limitations are explicitly noted for Video-ChatGPT.
8 Potential Risks
Video-ChatGPT should be used cautiously to address misuse, fairness, transparency, privacy, and residual bias. Bias may persist despite efforts to minimize it during dataset creation and may influence visual-content understanding and responses.
- Use of Video-ChatGPT requires safeguards against misuse and attention to fairness, transparency, and user privacy.
- Residual bias may persist despite efforts to minimize bias during dataset creation.
- Potential biases may subtly influence the model’s understanding and responses to visual content.
9 Use of Data and AI Assistant
The dataset is curated from a subset of ActivityNet-200 and distributed under the MIT License. The work also uses GPT models under OpenAI terms and releases created datasets under the MIT License.
- The dataset is based on a subset of the ActivityNet-200 dataset.
- ActivityNet-200 is distributed under the MIT License for research use.
- Datasets created in this work are released under the MIT License, while GPT model use follows OpenAI terms.
10 Human Annotations
The semi-automatic dataset curation process includes human annotation guided by concise video-caption ground truths. Annotators enrich captions with comprehensive temporal and spatial descriptions while correcting tone and bias.
- Human annotators receive concise video-caption ground truths as annotation inputs.
- Annotators enrich captions with comprehensive descriptions of video content.Instructions emphasize temporal and spatial details.
- Annotators are instructed to neutralize tone and biases during correction.
11 Qualitative Evaluation
The model is evaluated qualitatively across diverse open-ended video question-answering tasks and videos from ActivityNet and YouTube. It demonstrates proficiency in comprehending video content and generating accurate responses.
- The evaluation uses diverse videos sourced from ActivityNet and YouTube for open-ended video question-answering tasks.
- The tasks cover video reasoning, creative and generative tasks, spatial understanding, action recognition, conversation, question answering, and temporal understanding.
- The model demonstrates proficiency in comprehending video content and generating accurate responses.