activity
20232026
most citedBox-Level Active Detection

1 citations · 1 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models

Yiwen Liang, Hui Chen, Yizhe Xiong +7

Test-time adaptation (TTA) has been widely explored in single-label recognition, effectively mitigating distribution shifts, especially when combined with vision-language models. H…

eess.IV2026

A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation

Siyuan Ma, Liang He, Mengying Zhu +14

Missing or degraded sequences can limit prostate multiparametric MRI. We developed MSCNet, a sequence-conditioned cross-modal generative framework for reconstructing unavailable co…

cs.CV2026

Attention-Spectrum Regularization for Replay-Free Continual Multimodal LLMs

Chuangxin Zhao, Canran Xiao, Siyuan Ma +5

Multimodal large language models (MLLMs) are increasingly required to adapt to non-stationary streams of visual domains, question types, and user instructions, yet continual fine-t…

cs.SE2026

ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems

Junyi Lu, Mengyao Lyu, Jiahui Wu +6

The open-source ecosystem on GitHub lacks a systematic hierarchical taxonomy of software repositories. GitHub Topics, the dominant organizational mechanism, is flat, inconsistent,…

cs.CV2025

Advancing Reliable Test-Time Adaptation of Vision-Language Models under Visual Variations

Yiwen Liang, Hui Chen, Yizhe Xiong +7

Vision-language models (VLMs) exhibit remarkable zero-shot capabilities but struggle with distribution shifts in downstream tasks when labeled data is unavailable, which has motiva…

cs.CV2025

An Embarrassingly Simple Data Selection Strategy for Million-Scale Multi-Modal Instruction-Tuning

Mengyao Lyu, Yan Li, Huasong Zhong +5

Recent research demonstrates that pretrained large language models (LLMs) require minimal but carefully curated data for instruction-tuning. Building on LLMs, multi-modal models fu…

cs.CV2024

Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence

Mengyao Lyu, Tianxiang Hao, Xinhao Xu +4

Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Acti…

cs.CV2023

One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications

Mengyao Lyu, Yuhong Yang, Haiwen Hong +6

The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing…

cs.CV2023

Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability

Tianxiang Hao, Mengyao Lyu, Hui Chen +4

With the advancement of large pre-trained vision-language models, effectively transferring the knowledge embedded within these foundational models to downstream tasks has become a…

cs.CV2023★ 1 cited

Box-Level Active Detection

Mengyao Lyu, Jundong Zhou, Hui Chen +7

Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection. However, the widely used active detection benchma…