4 citations · 6 across the 14 of their papers we have counts for
10 papers · 1 filter
A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data
Yinong Wang, Jianwen Chen, Zhou Chen +28
We developed BrainVLM to classify all 12 World Health Organization (WHO) 2021 brain tumor types. BrainVLM integrates an uncertainty quantification strategy to indicate prediction r…
Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation
Xing Xie, Jiawei Liu, Shijun Zhou +4
We propose Discrete Diffusion Bridges (DDB), a novel framework designed to resolve the fundamental spatiotemporal misalignment of standard discrete diffusion in image translation a…
Discrete Diffusion Models with MLLMs for Unified Medical Multimodal Generation
Jiawei Mao, Yuhan Wang, Lifeng Chen +6
Recent advances in generative medical models are constrained by modality-specific scenarios that hinder the integration of complementary evidence from imaging, pathology, and clini…
DSKC: Domain Style Modeling with Adaptive Knowledge Consolidation for Exemplar-free Lifelong Person Re-Identification
Shiben Liu, Mingyue Xu, Huijie Fan +3
Lifelong Person Re-identification (LReID) aims to continuously match individuals across camera views from sequential data streams. Existing LReID methods often ignore domain-specif…
ATSTrack: Enhancing Visual-Language Tracking by Aligning Temporal and Spatial Scales
Yihao Zhen, Qiang Wang, Yu Qiao +2
A main challenge of Visual-Language Tracking (VLT) is the misalignment between visual inputs and language descriptions caused by target movement. Previous trackers have explored ma…
Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification
Shiben Liu, Huijie Fan, Qiang Wang +3
Lifelong Person Re-identification (LReID) suffers from a key challenge in preserving old knowledge while adapting to new information. The existing solutions include rehearsal-based…
Unleashing the Potential of Large Language Models for Text-to-Image Generation through Autoregressive Representation Alignment
Xing Xie, Jiawei Liu, Ziyue Lin +4
We present Autoregressive Representation Alignment (ARRA), a new training framework that unlocks global-coherent text-to-image generation in autoregressive LLMs without architectur…
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
Zhiheng Cheng, Qingyue Wei, Hongru Zhu +4
The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However, its application in medic…
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection
Weibo Jiang, Weihong Ren, Jiandong Tian +3
Human-Object Interaction (HOI) detection plays a vital role in scene understanding, which aims to predict the HOI triplet in the form of <human, object, action>. Existing methods m…
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning
Peiran Xu, Zeyu Wang, Jieru Mei +4
Federated learning (FL) is an emerging paradigm in machine learning, where a shared model is collaboratively learned using data from multiple devices to mitigate the risk of data l…