most citedStreamingVLM: Real-Time Understanding for Infinite Video Streams

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20261 cited

StreamingVLM: Real-Time Understanding for Infinite Video Streams

Ruyi Xu, Guangxuan Xiao, Yukang Chen +3

Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-infinite video streams without escalati…

cs.CV2026

Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation

Shuo Yang, Haocheng Xi, Yilong Zhao +10

Diffusion Transformers (DiTs) are essential for video generation but suffer from significant latency due to the quadratic complexity of attention. By computing only critical tokens…

cs.CV2026

Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation

Zhuoyang Zhang, Luke J. Huang, Chengyue Wu +4

We present Locality-aware Parallel Decoding (LPD) to accelerate autoregressive image generation. Traditional autoregressive image generation relies on next-patch prediction, a memo…

cs.CV2026

StreamDiffusionV2: A Streaming System for Dynamic and Interactive Video Generation

Tianrui Feng, Zhi Li, Shuo Yang +11

Generative models are reshaping the live-streaming industry by redefining how content is created, styled, and delivered. Previous image-based streaming diffusion models have powere…

cs.CV2025

Radial Attention: Sparse Attention with Energy Decay for Long Video Generation

Xingyang Li, Muyang Li, Tianle Cai +11

Recent advances in diffusion models have enabled high-quality video generation, but the additional temporal dimension significantly increases computational costs, making training a…