6 papers · 1 filter
FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation
Junkang Zhou, Yefei He, Feng Chen +2
Large-scale autoregressive models have demonstrated remarkable capabilities in image generation. However, their sequential raster-scan decoding relies on strictly next-token predic…
OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs
Feng Chen, Yefei He, Shaoxuan He +9
Existing sparse attention methods primarily target inference-time acceleration by selecting critical tokens under predefined sparsity patterns. However, they often fail to bridge t…
Neighboring Autoregressive Modeling for Efficient Visual Generation
Yefei He, Yuanyu He, Shaoxuan He +4
Visual autoregressive models typically adhere to a raster-order ``next-token prediction" paradigm, which overlooks the spatial and temporal locality inherent in visual content. Spe…
ZipAR: Parallel Auto-regressive Image Generation through Spatial Locality
Yefei He, Feng Chen, Yuanyu He +4
In this paper, we propose ZipAR, a training-free, plug-and-play parallel decoding framework for accelerating auto-regressive (AR) visual generation. The motivation stems from the o…
Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens
Feng Chen, Chenhui Gou, Jing Liu +6
As multimodal large language models (MLLMs) advance rapidly, rigorous evaluation has become essential, providing further guidance for their development. In this work, we focus on a…
ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification
Yefei He, Feng Chen, Jing Liu +4
The efficiency of large vision-language models (LVLMs) is constrained by the computational bottleneck of the attention mechanism during the prefill phase and the memory bottleneck…