Chen Zhao
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    • Invertible Diffusion Models for Inverse Problems
    • Video Understanding for Embodied AI
    • Long-form Video Understanding in the 2020s
    • 神经网络可逆化：大模型微调的高效显存优化策略
    • Long-form Video Understanding in the 2020s
    • Reversifying Neural Networks: Efficient Memory Optimization Strategies for Finetuning Large Models
    • Towards More Realistic Continual Learning at Scale
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    • Challenges and Advances in Long-form Video Understanding
    • Towards Long-form Video Understanding
    • Challenges and Innovation for Long-form Video Understanding: Compute, Algorithm, and Data
    • Research Highlights at IVUL with a Focus on Video Understanding
    • Towards Long-form Video Understanding
    • A Simple and Effective Approach for Long-form Video Understanding
    • Detecting Actions in Videos via Graph Convolutional Networks
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    • First place, Ego4D Visual Queries 3D
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    • First place, Epic-Kitchens audio-based interaction detection
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    • First place, Visual Queries 3D Localization Challenge in Ego4D Workshop
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    • BOLT: Boost Large Vision-Language Model Without Training for Long-form Video Understanding
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    • ETAD: Training Action Detection End to End on a Laptop
    • Just a Glimpse: Rethinking Temporal Information for Video Continual Learning
    • Owl (observe, watch, listen): Localizing actions in egocentric video via audiovisual temporal context
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    • Evaluation of Diverse Convolutional Neural Networks and Training Strategies for Wheat Leaf Disease Identification with Field-Acquired Photographs
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    • ThumbNet: One Thumbnail Image Contains All You Need for Recognition
    • Improve Baseline for Temporal Action Detection: HACS Challenge 2020 Solution of IVUL‑KAUST team
    • G‑TAD: Sub‑Graph Localization for Temporal Action Detection
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    • BoostNet: A Structured Deep Recursive Network to Boost Image Deblocking
    • Better and Faster, when ADMM Meets CNN: Compressive-sensed Image Reconstruction
    • Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior
    • Video Compressive Sensing Reconstruction via Reweighted Residual Sparsity
    • CONCOLOR: COnstrained Non-Convex Low-Rank Model for Image Deblocking
    • Nonconvex Lp Nuclear Norm based ADMM Framework for Compressive Sensing
    • Compressive-Sensed Image Coding via Stripe-based DPCM
    • A Dual Structured-Sparsity Model for Compressive-Sensed Video Reconstruction
    • 基于云数据的高效图像编码方法
    • Thousand to one: An image compression system via cloud search
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    • Video Compressive Sensing via Structured Laplacian Modelling
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    • Machine Learning (CS229), KAUST
    • Deep Learning for Visual Computing (CS323), KAUST
    • Video understanding, KAUST
    • Machine learning, KAUST
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    • Deep learning, KAUST
    • Deep learning, KAUST
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A Simple and Effective Approach for Long-form Video Understanding

February 20, 2023 · 1 min read
talks

Spotlight presentation, Rising Star in AI Symposium, KAUST, Saudi Arabia

Last updated on January 10, 2026
Chen Zhao
Authors
Chen Zhao
Research Scientist
← Towards Long-form Video Understanding December 1, 2023
Detecting Actions in Videos via Graph Convolutional Networks November 30, 2021 →

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