Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing
AutoGaze autoregressively selects multi-scale video patches, reducing redundancy and enhancing efficiency, enabling 1K-frame 4K video processing.
Key Findings
Methodology
AutoGaze is a lightweight module that autoregressively selects multi-scale video patches to reduce redundancy. It is trained using next-token prediction and reinforcement learning to select a minimal set of patches that can reconstruct the video within a user-specified error threshold. This method significantly reduces the number of visual tokens and accelerates the processing speed of vision transformers and multi-modal large language models.
Key Results
- AutoGaze reduces the number of visual tokens by 4 to 100 times in videos with different frame rates and resolutions while maintaining downstream multi-modal large language model performance. This results in up to 19 times speedup for vision transformers and multi-modal large language models.
- On the VideoMME benchmark, AutoGaze achieved a performance of 67.0%, surpassing strong multi-modal large language models such as Qwen2.5-VL.
- In the newly introduced high-resolution long video QA benchmark HLVid, a multi-modal large language model scaled with AutoGaze improved over the baseline by 10.1% and outperformed the previous best model by 4.5%.
Significance
AutoGaze significantly enhances the processing capability of multi-modal large language models on long and high-resolution videos by effectively reducing redundancy. This method not only improves the efficiency of existing models but also opens up possibilities for handling more complex video data, filling the gap in high-resolution long video processing.
Technical Contribution
The technical contribution of AutoGaze lies in its innovative use of autoregressive methods to select multi-scale patches, significantly reducing the number of visual tokens. Unlike existing methods that prune tokens inside the model or between the vision transformer and the large language model, AutoGaze removes redundancy directly at the input stage, enhancing overall efficiency.
Novelty
AutoGaze introduces the first autoregressive multi-scale patch selection method, distinguishing itself from previous heuristic or computationally intensive redundancy removal methods. Its innovation lies in optimizing patch selection through reinforcement learning, ensuring maximum efficiency while preserving information.
Limitations
- AutoGaze may perform suboptimally in extremely complex or highly dynamic videos, as these scenarios require higher detail retention.
- The method performs well on training data but may face generalization issues on unseen video styles or semantics.
- Due to its reliance on reinforcement learning and autoregressive selection, AutoGaze's training process can be complex and time-consuming.
Future Work
Future research directions include optimizing the training efficiency of AutoGaze, exploring its generalization capabilities on more unseen video styles and semantics. Additionally, research could focus on applying it to real-time video processing scenarios and testing on larger-scale video datasets.
AI Executive Summary
In the field of video understanding, existing multi-modal large language models have made progress in general video understanding but face challenges when dealing with long, high-resolution videos. This is because these models process every pixel equally in their vision transformers or large language models, failing to effectively remove spatiotemporal redundancy in videos.
AutoGaze is an innovative lightweight module designed to address this issue. By autoregressively selecting multi-scale video patches, AutoGaze removes redundant patches before processing, thereby reducing the number of visual tokens. It is trained using next-token prediction and reinforcement learning to ensure video reconstruction within a user-specified error threshold.
The core technical principle of AutoGaze lies in its autoregressive selection mechanism. Similar to how humans track eye movements when watching videos, AutoGaze intelligently selects informative regions while ignoring static backgrounds, enabling efficient processing of high-frame-rate, high-resolution video streams.
Experimental results show that AutoGaze reduces the number of visual tokens by 4 to 100 times in videos with different frame rates and resolutions while maintaining downstream multi-modal large language model performance. This results in up to 19 times speedup for vision transformers and multi-modal large language models. On the VideoMME benchmark, AutoGaze achieved a performance of 67.0%, surpassing strong multi-modal large language models such as Qwen2.5-VL.
Moreover, AutoGaze introduces the first high-resolution long video QA benchmark, HLVid. In this benchmark, a multi-modal large language model scaled with AutoGaze improved over the baseline by 10.1% and outperformed the previous best model by 4.5%. This demonstrates AutoGaze's significant advantage in handling complex video data.
Despite the significant advancements AutoGaze brings to video understanding, it may perform suboptimally in extremely complex or highly dynamic videos. Additionally, due to its reliance on reinforcement learning and autoregressive selection, the training process of AutoGaze can be complex and time-consuming. Future research directions include optimizing its training efficiency and exploring its generalization capabilities on more unseen video styles and semantics.
Deep Analysis
Background
Video understanding technology has made significant progress in recent years, particularly driven by multi-modal large language models (MLLMs). These models, by combining visual and language information, have excelled in tasks such as video question answering and caption generation. However, with the increasing complexity of video content, especially the emergence of long-duration and high-resolution videos, existing methods face significant challenges in processing these videos. This is primarily due to the issue of spatiotemporal redundancy in videos, where a large amount of static background and repetitive information leads to wasted computational resources. Traditional methods often process every pixel equally, failing to effectively remove these redundant information. Therefore, efficiently processing long-duration and high-resolution videos without losing information has become an urgent problem to solve.
Core Problem
Existing multi-modal large language models face significant computational bottlenecks when processing long-duration and high-resolution videos. These models typically rely on vision transformers (ViTs) or large language models (LLMs) to process every pixel, failing to effectively remove spatiotemporal redundancy in videos. This leads to wasted computational resources and limits the scalability of models on long and high-resolution videos. Additionally, existing methods often rely on heuristic redundancy removal strategies, which perform poorly in processing complex videos. Therefore, designing a method that can intelligently select informative regions and ignore redundant information becomes a key issue.
Innovation
AutoGaze introduces an innovative autoregressive multi-scale patch selection method to address the issue of spatiotemporal redundancy in videos. Its core innovations include:
1. Autoregressive selection mechanism: AutoGaze autoregressively selects multi-scale video patches, intelligently selecting informative regions while ignoring static backgrounds.
2. Multi-scale patch selection: By selecting patches of different scales, AutoGaze can reduce the number of visual tokens without losing information.
3. Reinforcement learning optimization: AutoGaze optimizes patch selection through reinforcement learning, ensuring maximum efficiency while preserving information. These innovations enable AutoGaze to efficiently process high-frame-rate, high-resolution video streams.
Methodology
The methodology of AutoGaze includes the following key steps:
- �� Data preprocessing: Preprocess the input video and segment it into multi-scale patches.
- �� Autoregressive selection: Select informative patches through an autoregressive mechanism, ignoring redundant information.
- �� Reinforcement learning training: Train using next-token prediction and reinforcement learning to optimize patch selection strategy.
- �� Multi-scale patch reconstruction: Reconstruct the video based on selected patches, ensuring video reconstruction within a user-specified error threshold.
- �� Integration into existing models: Integrate AutoGaze into existing vision transformers and multi-modal large language models to improve processing efficiency.
Experiments
The experimental design includes using multiple benchmark datasets to evaluate the performance of AutoGaze. The main datasets used include VideoMME and the newly introduced HLVid benchmark. The experiments compare the performance of AutoGaze with existing multi-modal large language models in processing long and high-resolution videos. Key hyperparameters include the scale of patch selection and the step size of autoregressive selection. Additionally, ablation studies are conducted to verify the generalization capability of AutoGaze on different video styles and semantics.
Results
Experimental results show that AutoGaze reduces the number of visual tokens by 4 to 100 times in videos with different frame rates and resolutions while maintaining downstream multi-modal large language model performance. This results in up to 19 times speedup for vision transformers and multi-modal large language models. On the VideoMME benchmark, AutoGaze achieved a performance of 67.0%, surpassing strong multi-modal large language models such as Qwen2.5-VL. Additionally, in the newly introduced high-resolution long video QA benchmark HLVid, a multi-modal large language model scaled with AutoGaze improved over the baseline by 10.1% and outperformed the previous best model by 4.5%.
Applications
The application scenarios of AutoGaze include:
1. Video question answering: Enhance the performance of video QA systems by efficiently processing long and high-resolution videos.
2. Video caption generation: Quickly generate high-quality video captions without losing information, applicable to movie and TV production.
3. Real-time video analysis: Achieve efficient analysis of real-time video streams by reducing redundant information, applicable to surveillance and autonomous driving.
Limitations & Outlook
Despite the significant advancements AutoGaze brings to video understanding, it may perform suboptimally in extremely complex or highly dynamic videos, as these scenarios require higher detail retention. Additionally, due to its reliance on reinforcement learning and autoregressive selection, the training process of AutoGaze can be complex and time-consuming. Future research directions include optimizing its training efficiency and exploring its generalization capabilities on more unseen video styles and semantics.
Plain Language Accessible to non-experts
Imagine you're watching a soccer match. You don't need to focus on every single detail all the time; instead, you pay attention to where the ball is, the players' movements, and key moments in the game. AutoGaze is like a smart viewer that automatically selects those important parts of the match while ignoring the less important details. This not only makes the viewing experience smoother but also saves a lot of time and effort.
In video processing, traditional methods are like a tireless viewer trying to focus on every detail, which is not only inefficient but also wastes a lot of computational resources. AutoGaze, on the other hand, uses a technique called autoregressive selection, acting like a smart viewer that only focuses on the truly important parts.
The benefit of this approach is that it can significantly reduce the amount of data that needs to be processed without losing important information. It's like watching the highlights of a game to understand the essence of the entire match without having to watch it from start to finish.
In summary, AutoGaze intelligently selects video patches, making video processing more efficient, just like a discerning viewer who gets the most information in the shortest time.
ELI14 Explained like you're 14
Hey there! Have you ever wondered why we don't need to focus on every single detail when watching a video? That's because our brains automatically choose the important parts and ignore the less important details. AutoGaze is a super smart tool that helps computers watch videos as smartly as we do!
Imagine you're playing a game. You don't need to pay attention to every pixel all the time; instead, you notice where the enemies are and what power-ups you can use. AutoGaze is like an assistant in the game that automatically selects the important game scenes and ignores the less important backgrounds.
What's the benefit of this? It's like taking notes in school, only writing down the key points the teacher says instead of copying every word. This not only saves time but also makes it easier to understand and remember the important information.
So, AutoGaze makes computers as smart as us when processing videos, only focusing on the truly important parts! Isn't that cool?
Glossary
Autoregressive
A method that generates or selects data step-by-step, with each step depending on the previous ones.
AutoGaze uses an autoregressive method to select video patches.
Multi-modal Large Language Model
A large model that combines visual and language information for tasks like video question answering and caption generation.
AutoGaze enhances the performance of multi-modal large language models on high-resolution videos.
Vision Transformer
A deep learning model used for image and video processing, capable of efficiently handling visual information.
AutoGaze improves the efficiency of vision transformers by reducing the number of visual tokens.
Reinforcement Learning
A method of training models through a reward mechanism to improve performance in specific tasks.
AutoGaze optimizes patch selection strategy through reinforcement learning.
Spatiotemporal Redundancy
Repetitive or unimportant information in videos that leads to wasted computational resources.
AutoGaze enhances video processing efficiency by removing spatiotemporal redundancy.
Patch Selection
The process of selecting important patches in video processing to reduce data volume.
AutoGaze autoregressively selects important multi-scale patches.
Error Threshold
A user-specified allowable error range used to control the precision of video reconstruction.
AutoGaze reconstructs videos within a user-specified error threshold.
Benchmark
A standard dataset or task used to evaluate model performance.
AutoGaze performs excellently on the VideoMME and HLVid benchmarks.
Ablation Study
An evaluation method that assesses the impact of removing or modifying certain parts of a model on overall performance.
Ablation studies were conducted to verify the effectiveness of AutoGaze.
High Resolution
A video or image with a large number of pixels, providing richer details.
AutoGaze efficiently processes high-resolution videos.
Open Questions Unanswered questions from this research
- 1 How can AutoGaze's performance be further optimized in extremely complex or highly dynamic videos? Existing methods may perform suboptimally in these scenarios due to the need for higher detail retention.
- 2 What is AutoGaze's generalization capability on unseen video styles and semantics? Although it performs well on training data, it may face generalization issues on some unseen videos.
- 3 How can the training efficiency of AutoGaze be improved? Due to its reliance on reinforcement learning and autoregressive selection, AutoGaze's training process can be complex and time-consuming.
- 4 Can AutoGaze be applied to real-time video processing scenarios? Current research mainly focuses on offline video processing, and real-time applications may face computational resource constraints.
- 5 How does AutoGaze perform on larger-scale video datasets? Existing experiments mainly focus on specific benchmarks, and testing on larger-scale datasets has not yet been conducted.
Applications
Immediate Applications
Video Question Answering Systems
Enhance the performance of video QA systems by efficiently processing long and high-resolution videos, applicable in education and entertainment.
Video Caption Generation
Quickly generate high-quality video captions without losing information, applicable to movie and TV production.
Real-time Video Analysis
Achieve efficient analysis of real-time video streams by reducing redundant information, applicable to surveillance and autonomous driving.
Long-term Vision
Intelligent Video Editing
Utilize AutoGaze's patch selection technology to achieve automated video editing and clipping, improving video production efficiency.
Virtual Reality Applications
Enhance user experience in virtual reality environments by efficiently processing high-resolution videos, achieving more realistic virtual scenes.
Abstract
Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos -- they process every pixel equally in their vision transformers (ViTs) or LLMs despite significant spatiotemporal redundancy. We introduce AutoGaze, a lightweight module that removes redundant patches before processed by a ViT or an MLLM. Trained with next-token prediction and reinforcement learning, AutoGaze autoregressively selects a minimal set of multi-scale patches that can reconstruct the video within a user-specified error threshold, eliminating redundancy while preserving information. Empirically, AutoGaze reduces visual tokens by 4x-100x and accelerates ViTs and MLLMs by up to 19x, enabling scaling MLLMs to 1K-frame 4K-resolution videos and achieving superior results on video benchmarks (e.g., 67.0% on VideoMME). Furthermore, we introduce HLVid: the first high-resolution, long-form video QA benchmark with 5-minute 4K-resolution videos, where an MLLM scaled with AutoGaze improves over the baseline by 10.1% and outperforms the previous best MLLM by 4.5%. Project page: https://autogaze.github.io/.
References (20)
VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling
Xinhao Li, Yi Wang, Jiashuo Yu et al.
EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding
K. Mangalam, Raiymbek Akshulakov, J. Malik
LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding
Haoning Wu, Dongxu Li, Bei Chen et al.
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
Chaoyou Fu, Yuhan Dai, Yondong Luo et al.
NVILA: Efficient Frontier Visual Language Models
Zhijian Liu, Ligeng Zhu, Baifeng Shi et al.
GPT-4o System Card
OpenAI Aaron Hurst, Adam Lerer, Adam P. Goucher et al.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, N. Savinov, Denis Teplyashin et al.
LLaVA-OneVision: Easy Visual Task Transfer
Bo Li, Yuanhan Zhang, Dong Guo et al.
Qwen2.5-VL Technical Report
Shuai Bai, Keqin Chen, Xuejing Liu et al.
ViViT: A Video Vision Transformer
Anurag Arnab, Mostafa Dehghani, G. Heigold et al.
Better & Faster Large Language Models via Multi-token Prediction
Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière et al.
InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
Yi Wang, Kunchang Li, Xinhao Li et al.
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
João Carreira, Andrew Zisserman
Understanding Human Hands in Contact at Internet Scale
Dandan Shan, Jiaqi Geng, Michelle Shu et al.
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Adam Suma, Sam Dauncey
Understanding.
M. George
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mahmoud Assran, Adrien Bardes, David Fan et al.
VisionZip: Longer is Better but Not Necessary in Vision Language Models
Senqiao Yang, Yukang Chen, Zhuotao Tian et al.
Anticipating Visual Representations from Unlabeled Video
Carl Vondrick, H. Pirsiavash, A. Torralba
Apollo: An Exploration of Video Understanding in Large Multimodal Models
Orr Zohar, Xiaohan Wang, Yann Dubois et al.