6 papers · 1 filter
Stateful Token Reduction for Long-Video Hybrid VLMs
Jindong Jiang, Amala Sanjay Deshmukh, Kateryna Chumachenko +7
Token reduction accelerates long-video vision--language models (VLMs), but existing methods target Transformers, where reduction is treated as token pruning. We study token reducti…
Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders
Yitong Jiang, Hongjun Wang, Collin McCarthy +15
Vision foundation models are bottlenecked by the quadratic cost of self-attention, which limits usable resolution and increases the cost of large-scale pretraining. Subquadratic al…
STORM: Token-Efficient Long Video Understanding for Multimodal LLMs
Jindong Jiang, Xiuyu Li, Zhijian Liu +13
Recent advances in video-based multimodal large language models (Video-LLMs) have significantly improved video understanding by processing videos as sequences of image frames. Howe…
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Guo Chen, Zhiqi Li, Shihao Wang +16
We introduce Eagle 2.5, a family of frontier vision-language models (VLMs) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and h…
Feature-based Graph Attention Networks Improve Online Continual Learning
Adjovi Sim, Zhengkui Wang, Aik Beng Ng +3
Online continual learning for image classification is crucial for models to adapt to new data while retaining knowledge of previously learned tasks. This capability is essential to…
Parallel Sequence Modeling via Generalized Spatial Propagation Network
Hongjun Wang, Wonmin Byeon, Jiarui Xu +6
We present the Generalized Spatial Propagation Network (GSPN), a new attention mechanism optimized for vision tasks that inherently captures 2D spatial structures. Existing attenti…