7 citations · 17 across the 16 of their papers we have counts for
12 papers · 1 filter
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding
Shihao Wang, Shilong Liu, Yuanguo Kuang +10
Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into multiple 1D tokens that are l…
Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline
Guo Chen, Lidong Lu, Yicheng Liu +17
While datasets for video understanding have scaled to hour-long durations, they typically consist of densely concatenated clips that differ from natural, unscripted daily life. To…
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…
PhyCritic: Multimodal Critic Models for Physical AI
Tianyi Xiong, Shihao Wang, Guilin Liu +5
With the rapid development of large multimodal models, reliable judge and critic models have become essential for open-ended evaluation and preference alignment, providing pairwise…
Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought
Yunze Man, De-An Huang, Guilin Liu +6
Recent advances in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in vision-language tasks, yet they often struggle with vision-centric scenario…
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…
Slow-Fast Architecture for Video Multi-Modal Large Language Models
Min Shi, Shihao Wang, Chieh-Yun Chen +6
Balancing temporal resolution and spatial detail under limited compute budget remains a key challenge for video-based multi-modal large language models (MLLMs). Existing methods ty…
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…
AIDE: Agentically Improve Visual Language Model with Domain Experts
Ming-Chang Chiu, Fuxiao Liu, Karan Sapra +5
The enhancement of Visual Language Models (VLMs) has traditionally relied on knowledge distillation from larger, more capable models. This dependence creates a fundamental bottlene…
Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models
Zhiqi Li, Guo Chen, Shilong Liu +24
Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most…
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
Min Shi, Fuxiao Liu, Shihao Wang +13
The ability to accurately interpret complex visual information is a crucial topic of multimodal large language models (MLLMs). Recent work indicates that enhanced visual perception…
DiffiT: Diffusion Vision Transformers for Image Generation
Ali Hatamizadeh, Jiaming Song, Guilin Liu +2
Diffusion models with their powerful expressivity and high sample quality have achieved State-Of-The-Art (SOTA) performance in the generative domain. The pioneering Vision Transfor…