3 citations · 4 across the 17 of their papers we have counts for
6 papers · 1 filter
Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning
Jiahe Chen, Qian Shao, Qiyuan Chen +4
Open-set semi-supervised learning aims to leverage unlabeled data that may contain out-of-distribution outliers while maintaining performance on in-distribution classes. Existing m…
Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training
Anglin Liu, Ruichao Chen, Yi Lu +2
Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer from a critical perceptual deficit: geo…
STORM: Benchmarking Visual Rating of MLLMs with a Comprehensive Ordinal Regression Dataset
Jinhong Wang, Shuo Tong, Jian liu +6
Visual rating is an essential capability of artificial intelligence (AI) for multi-dimensional quantification of visual content, primarily applied in ordinal regression (OR) tasks…
Curing Semantic Drift: A Dynamic Approach to Grounding Generation in Large Vision-Language Models
Jiahe Chen, Jiaying He, Qiyuan Chen +6
Large Vision-Language Models (LVLMs) face a tug-of-war between powerful linguistic priors and visual evidence, often leading to \emph{semantic drift}: a progressive detachment from…
OrderChain: Towards General Instruct-Tuning for Stimulating the Ordinal Understanding Ability of MLLM
Jinhong Wang, Shuo Tong, Jian liu +7
Despite the remarkable progress of multimodal large language models (MLLMs), they continue to face challenges in achieving competitive performance on ordinal regression (OR; a.k.a.…
KAER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation
Shangde Gao, Yichao Fu, Ke Liu +2
Recently, many foundation models for medical image analysis such as MedSAM, SwinUNETR have been released and proven to be useful in multiple tasks. However, considering the inheren…