1 citations · 1 across the 11 of their papers we have counts for
11 papers
MetaDent: Labeling Clinical Images for Vision-Language Models in Dentistry
Meng-Xun Li, Wen-Hui Deng, Zhi-Xing Wu +6
Vision-Language Models (VLMs) have demonstrated significant potential in medical image analysis, yet their application in intraoral photography remains largely underexplored due to…
On the Faithfulness of Visual Thinking: Measurement and Enhancement
Zujing Liu, Junwen Pan, Qi She +2
Recent large vision-language models (LVLMs) can generate vision-text multimodal chain-of-thought (MCoT) traces after reinforcement fine-tuning (RFT). However, we observe that the v…
Intensity Field Decomposition for Tissue-Guided Neural Tomography
Meng-Xun Li, Jin-Gang Yu, Yuan Gao +2
Cone-beam computed tomography (CBCT) typically requires hundreds of X-ray projections, which raises concerns about radiation exposure. While sparse-view reconstruction reduces the…
Fuse Before Transfer: Knowledge Fusion for Heterogeneous Distillation
Guopeng Li, Qiang Wang, Ke Yan +3
Most knowledge distillation (KD) methodologies predominantly focus on teacher-student pairs with similar architectures, such as both being convolutional neural networks (CNNs). How…
DMTG: One-Shot Differentiable Multi-Task Grouping
Yuan Gao, Shuguo Jiang, Moran Li +2
We aim to address Multi-Task Learning (MTL) with a large number of tasks by Multi-Task Grouping (MTG). Given N tasks, we propose to simultaneously identify the best task groups fro…
Towards Human-Level 3D Relative Pose Estimation: Generalizable, Training-Free, with Single Reference
Yuan Gao, Yajing Luo, Junhong Wang +2
Humans can easily deduce the relative pose of a previously unseen object, without labeling or training, given only a single query-reference image pair. This is arguably achieved by…
Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient
Yuan Gao, Zujing Liu, Weizhong Zhang +2
Recent Large-Language Models (LLMs) pruning methods typically operate at the post-training phase without the expensive weight finetuning, however, their pruning criteria often rely…
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost
Yuan Gao, Weizhong Zhang, Wenhan Luo +4
We aim at exploiting additional auxiliary labels from an independent (auxiliary) task to boost the primary task performance which we focus on, while preserving a single task infere…
Dual Relation Mining Network for Zero-Shot Learning
Jinwei Han, Yingguo Gao, Zhiwen Lin +4
Zero-shot learning (ZSL) aims to recognize novel classes through transferring shared semantic knowledge (e.g., attributes) from seen classes to unseen classes. Recently, attention-…
Anchor-based Robust Finetuning of Vision-Language Models
Jinwei Han, Zhiwen Lin, Zhongyisun Sun +5
We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as…
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
Yu Ming, Zihao Wu, Jie Yang +7
Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this…