activity
20202026
most citedMatrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning

34 citations · 93 across the 29 of their papers we have counts for

collaborators

29 papers

cs.CV2026

Miles: Metric Learning with Expandable Subspace for Pre-Trained Model-Based Class-Incremental Learning

Kai Jiang, Zisong Lin, Hongyuan Zhang +2

Class Incremental Learning (CIL) aims to learn new concepts consistently from a data stream without forgetting. Unlike typical CIL methods which need to learn a model from scratch,…

cs.LG2026

MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification

Da Zhang, Bingyu Li, Zhiyuan Zhao +3

Medical time series are central to healthcare, enabling continuous monitoring and supporting timely clinical decisions. Despite recent progress, existing methods struggle to jointl…

cs.CR2026

Safeguarding Text-to-Image Generative Models Against Unauthorized Knowledge Distillation

Yilan Gao, Sida Huang, Hongyuan Zhang +1

Closed-weight generative services are increasingly deployed through query-based APIs, where users can obtain generated outputs while model parameters remain inaccessible. However,…

cs.CV2026

AHAP: Reconstructing Arbitrary Humans from Arbitrary Perspectives with Geometric Priors

Xiaozhen Qiao, Wenjia Wang, Zhiyuan Zhao +4

Reconstructing 3D humans from images captured at multiple perspectives typically requires pre-calibration, like using checkerboards or MVS algorithms, which limits scalability and…

cs.CV2025

ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Discrete Diffusion Models

Ruishu Zhu, Zhihao Huang, Jiacheng Sun +3

Motivated by discrete diffusion's success in language-vision modeling, we explore its potential for multi-view generation, a task dominated by continuous approaches. We introduce V…

cs.LG2025

GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning

Yanchen Xu, Ziheng Jiao, Hongyuan Zhang +1

The Group Relative Policy Optimization (GRPO), a reinforcement learning method used to fine-tune large language models (LLMs), has proved its effectiveness in practical application…