7 citations · 7 across the 18 of their papers we have counts for
13 papers · 1 filter
Is Better Teacher Supervision Enough? Unlocking Student-side Learning in Multimodal On-Policy Distillation
Siyuan Liu, Kanghui Tian, Yue Duan +4
On-policy distillation (OPD) improves reasoning by providing token-level supervision from a teacher on a student's own trajectories. Existing methods primarily focus on enhancing t…
StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation
Jintao Guo, Lin Wang, Shumeng Li +5
Existing cross-subject fMRI decoding methods typically train a model on multiple scanned subjects and then adapt it to a new subject using substantial paired fMRI-image data. Howev…
One Token, Two Fates: A Unified Framework via Vision Token Manipulation Against MLLMs Hallucination
Zhan Fa, Yue Duan, Jian Zhang +2
Current training-free methods tackle MLLM hallucination with separate strategies: either enhancing visual signals or suppressing text inertia. However, these separate methods are i…
Duala: Dual-Level Alignment of Subjects and Stimuli for Cross-Subject fMRI Decoding
Shumeng Li, Jintao Guo, Jian Zhang +3
Cross-subject visual decoding aims to reconstruct visual experiences from brain activity across individuals, enabling more scalable and practical brain-computer interfaces. However…
Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
Shumeng Li, Jian Zhang, Lei Qi +3
Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled da…
Towards Perfection: Building Inter-component Mutual Correction for Retinex-based Low-light Image Enhancement
Luyang Cao, Han Xu, Jian Zhang +4
In low-light image enhancement, Retinex-based deep learning methods have garnered significant attention due to their exceptional interpretability. These methods decompose images in…
Correspondence as Video: Test-Time Adaption on SAM2 for Reference Segmentation in the Wild
Haoran Wang, Zekun Li, Jian Zhang +2
Large vision models like the Segment Anything Model (SAM) exhibit significant limitations when applied to downstream tasks in the wild. Consequently, reference segmentation, which…
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation
Qinghe Ma, Jian Zhang, Lei Qi +3
Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods…
Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation
Luyang Cao, Jianwei Li, Yinghuan Shi
Semi-supervised medical image segmentation (SSMIS) leverages unlabeled data to reduce reliance on manually annotated images. However, current SOTA approaches predominantly focus on…
Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting
Maochen Yang, Zekun Li, Jian Zhang +2
Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed…
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation
Qinghe Ma, Jian Zhang, Zekun Li +3
Large pretrained visual foundation models exhibit impressive general capabilities. However, the extensive prior knowledge inherent in these models can sometimes be a double-edged s…
WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Mining
Haoran Wang, Lian Huai, Wenbin Li +3
We have witnessed remarkable progress in foundation models in vision tasks. Currently, several recent works have utilized the segmenting anything model (SAM) to boost the segmentat…
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
Lihe Yang, Xiaogang Xu, Bingyi Kang +2
Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures. However, they are extremely hungry for delicate annotations t…