3 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained Perception in VLMs
Peng Liu, Haozhan Shen, Chunxin Fang +3
Vision-Language Models (VLMs) excel at high-level scene understanding but falter on fine-grained perception tasks requiring precise localization. This failure stems from a fundamen…
VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
Haozhan Shen, Peng Liu, Jingcheng Li +9
Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective…
OmChat: A Recipe to Train Multimodal Language Models with Strong Long Context and Video Understanding
Tiancheng Zhao, Qianqian Zhang, Kyusong Lee +7
We introduce OmChat, a model designed to excel in handling long contexts and video understanding tasks. OmChat's new architecture standardizes how different visual inputs are proce…
How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection
Yiyang Yao, Peng Liu, Tiancheng Zhao +5
Object detection (OD) in computer vision has made significant progress in recent years, transitioning from closed-set labels to open-vocabulary detection (OVD) based on large-scale…