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20242026
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cs.CV2026

OmniMem: Scalable and Adaptive Memory Retrieval for Long Video Generation

Lin Zhao, Yushu Wu, Yifan Gong +2

Autoregressive (AR) video generation extends videos by producing latent chunks sequentially, but scaling to long videos requires repeated access to a growing historical KV cache. E…

cs.CV2025

FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge

Xuan Shen, Weize Ma, Yufa Zhou +11

Auto-regressive (AR) models, initially successful in language generation, have recently shown promise in visual generation tasks due to their superior sampling efficiency. Unlike i…

cs.CV2025

DraftAttention: Fast Video Diffusion via Low-Resolution Attention Guidance

Xuan Shen, Chenxia Han, Yufa Zhou +7

Diffusion transformer-based video generation models (DiTs) have recently attracted widespread attention for their excellent generation quality. However, their computational cost re…

cs.CV2024

Fast and Memory-Efficient Video Diffusion Using Streamlined Inference

Zheng Zhan, Yushu Wu, Yifan Gong +7

The rapid progress in artificial intelligence-generated content (AIGC), especially with diffusion models, has significantly advanced development of high-quality video generation. H…

cs.CV2024

Lotus: learning-based online thermal and latency variation management for two-stage detectors on edge devices

Yifan Gong, Yushu Wu, Zheng Zhan +5

Two-stage object detectors exhibit high accuracy and precise localization, especially for identifying small objects that are favorable for various edge applications. However, the h…

cs.CV2024

Exploring Token Pruning in Vision State Space Models

Zheng Zhan, Zhenglun Kong, Yifan Gong +8

State Space Models (SSMs) have the advantage of keeping linear computational complexity compared to attention modules in transformers, and have been applied to vision tasks as a ne…