Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

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2-4x

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Talent Scout

B

Baifeng Shi

UC Berkeley

S

Stephanie Fu

UC Berkeley

L

Long Lian

UC Berkeley

H

Hanrong Ye

NVIDIA

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video_understanding experts on LinkedIn & GitHub

References (100)

[1]
FOCUS: Efficient Keyframe Selection for Long Video Understanding
2025Zi-Xuan Zhu, Hailun Xu et al.
[2]
OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM
2025Hanrong Ye, Chao-Han Huck Yang et al.
[3]
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
2025Weiyun Wang, Zhangwei Gao et al.
[4]
VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
2025Shihao Wang, Guo Chen et al.
[5]
Single-pass Adaptive Image Tokenization for Minimum Program Search
2025Shivam Duggal, Sanghyun Byun et al.
[6]
Scaling RL to Long Videos
2025Yukang Chen, Wei Huang et al.
[7]
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
2025Gheorghe Comanici, Eric Bieber et al.
[8]
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
2025Mahmoud Assran, Adrien Bardes et al.
[9]
MR. Video: "MapReduce" is the Principle for Long Video Understanding
2025Ziqi Pang, Yu-Xiong Wang
[10]
Perception Encoder: The best visual embeddings are not at the output of the network
2025Daniel Bolya, Po-Yao Huang et al.
[11]
Re-thinking Temporal Search for Long-Form Video Understanding
2025Jinhui Ye, Zihan Wang et al.
[12]
Understanding R1-Zero-Like Training: A Critical Perspective
2025Zi-Yan Liu, Changyu Chen et al.
[13]
Scaling Vision Pre-Training to 4K Resolution
2025Baifeng Shi, Boyi Li et al.
[14]
Improving LLM Video Understanding with 16 Frames Per Second
2025Yixuan Li, Changli Tang et al.
[15]
FastVID: Dynamic Density Pruning for Fast Video Large Language Models
2025Leqi Shen, Guoqiang Gong et al.
[16]
STORM: Token-Efficient Long Video Understanding for Multimodal LLMs
2025Jindong Jiang, Xiuyu Li et al.
[17]
Adaptive Keyframe Sampling for Long Video Understanding
2025Xi Tang, Jihao Qiu et al.
[18]
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
2025Michael Tschannen, Alexey Gritsenko et al.
[19]
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[20]
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2025Roman Bachmann, Jesse Allardice et al.

Showing 20 of 100 references

Founder's Pitch

"AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content."

video_understandingScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

3/4 signals

7.5

Series A Potential

4/4 signals

10

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Why It Matters

This research provides a method to significantly reduce computational costs in video processing by focusing computational resources on the most informative parts of a video, enabling high-resolution and long-duration video analysis that is feasible and efficient.

Product Angle

This could be productized as a tool or API that integrates with existing video processing suites to enhance efficiency and capability, particularly useful for media companies and any enterprises dealing with large-scale video content processing.

Disruption

This could replace current video processing and analysis systems that inefficiently sweep through entire video frames, leading to higher costs and slower processing.

Product Opportunity

The market for video content analysis tools is substantial, including applications in media, entertainment, security, and autonomous systems. Companies would pay for a solution that reduces computational costs while increasing processing speed and maintaining or boosting analytic accuracy.

Use Case Idea

Implement AutoGaze in video surveillance systems to enhance real-time monitoring capabilities by focusing on vital changes and movements rather than processing entire frames, reducing the need for extensive computational resources.

Science

AutoGaze uses a lightweight, 3M-parameter model to reduce input data for vision transformers by autoregressively selecting relevant video patches using a combination of a convolutional encoder and autoregressive transformer decoder. This method reduces redundancy in video frames, speeding up processing and reducing computational load without significant loss of information.

Method & Eval

AutoGaze demonstrated a reduction of video processing load by up to 100x while maintaining high performance, surpassing benchmarks like VideoMME with significant improvements in speed and efficiency.

Caveats

The primary limitation is the need for pre-training with substantial data to optimize the patch selection process. Additionally, integration with existing video processing systems may require significant upfront efforts.

Author Intelligence

Baifeng Shi

UC Berkeley

Stephanie Fu

UC Berkeley

Long Lian

UC Berkeley

Hanrong Ye

NVIDIA

David Eigen

Clarifai

Aaron Reite

Clarifai

Boyi Li

UC Berkeley, NVIDIA

Jan Kautz

NVIDIA

Song Han

MIT

David M. Chan

UC Berkeley

Pavlo Molchanov

NVIDIA

Trevor Darrell

UC Berkeley

Hongxu Yin

NVIDIA

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