8 papers
MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs
Jiyao Liu, Jinjie Wei, Wanying Qu +17
Medical Image Quality Assessment (IQA) serves as the first-mile safety gate for clinical AI, yet existing approaches remain constrained by scalar, score-based metrics and fail to r…
TempSamp-R1: Effective Temporal Sampling with Reinforcement Fine-Tuning for Video LLMs
Yunheng Li, Jing Cheng, Shaoyong Jia +4
This paper introduces TempSamp-R1, a new reinforcement fine-tuning framework designed to improve the effectiveness of adapting multimodal large language models (MLLMs) to video tem…
Revisiting Efficient Semantic Segmentation: Learning Offsets for Better Spatial and Class Feature Alignment
Shi-Chen Zhang, Yunheng Li, Yu-Huan Wu +2
Semantic segmentation is fundamental to vision systems requiring pixel-level scene understanding, yet deploying it on resource-constrained devices demands efficient architectures.…
A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models
Quan-Sheng Zeng, Yunheng Li, Qilong Wang +4
Visual token compression is critical for Large Vision-Language Models (LVLMs) to efficiently process high-resolution inputs. Existing methods that typically adopt fixed compression…
SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection
Yuxuan Li, Xiang Li, Yunheng Li +5
With the rapid advancement of remote sensing technology, high-resolution multi-modal imagery is now more widely accessible. Conventional Object detection models are trained on a si…
High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation
Quan-Sheng Zeng, Yunheng Li, Daquan Zhou +3
Open-vocabulary image segmentation has been advanced through the synergy between mask generators and vision-language models like Contrastive Language-Image Pre-training (CLIP). Pre…