5 citations · 7 across the 8 of their papers we have counts for
9 papers
LiMT: A Multi-task Liver Image Benchmark Dataset
Zhe Liu, Kai Han, Siqi Ma +10
Computer-aided diagnosis (CAD) technology can assist clinicians in evaluating liver lesions and intervening with treatment in time. Although CAD technology has advanced in recent y…
Frequency Domain Unlocks New Perspectives for Abdominal Medical Image Segmentation
Kai Han, Siqi Ma, Chengxuan Qian +4
Accurate segmentation of tumors and adjacent normal tissues in medical images is essential for surgical planning and tumor staging. Although foundation models generally perform wel…
Video-STAR: Reinforcing Open-Vocabulary Action Recognition with Tools
Zhenlong Yuan, Xiangyan Qu, Chengxuan Qian +8
Multimodal large language models (MLLMs) have demonstrated remarkable potential in bridging visual and textual reasoning, yet their reliance on text-centric priors often limits the…
CLIMD: A Curriculum Learning Framework for Imbalanced Multimodal Diagnosis
Kai Han, Chongwen Lyu, Lele Ma +5
Clinicians usually combine information from multiple sources to achieve the most accurate diagnosis, and this has sparked increasing interest in leveraging multimodal deep learning…
HAIF-GS: Hierarchical and Induced Flow-Guided Gaussian Splatting for Dynamic Scene
Jianing Chen, Zehao Li, Yujun Cai +7
Reconstructing dynamic 3D scenes from monocular videos remains a fundamental challenge in 3D vision. While 3D Gaussian Splatting (3DGS) achieves real-time rendering in static setti…
Adaptive Label Correction for Robust Medical Image Segmentation with Noisy Labels
Chengxuan Qian, Kai Han, Jianxia Ding +4
Deep learning has shown remarkable success in medical image analysis, but its reliance on large volumes of high-quality labeled data limits its applicability. While noisy labeled d…
DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning
Chengxuan Qian, Shuo Xing, Shawn Li +2
Multimodal representation learning aims to capture both shared and complementary semantic information across multiple modalities. However, the intrinsic heterogeneity of diverse mo…
DynCIM: Dynamic Curriculum for Imbalanced Multimodal Learning
Chengxuan Qian, Kai Han, Jiaxin Liu +6
Multimodal learning integrates complementary information from diverse modalities to enhance the decision-making process. However, the potential of multimodal collaboration remains…
Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization
Shuo Xing, Peiran Li, Yuping Wang +6
The emergence of large Vision Language Models (VLMs) has broadened the scope and capabilities of single-modal Large Language Models (LLMs) by integrating visual modalities, thereby…