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20242026
most citedQ-value Regularized Transformer for Offline Reinforcement Learning

4 citations · 11 across the 17 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

Deep Reprogramming Distillation for Medical Foundation Models

Siyuan Du, Yuhang Zhou, Haolin Li +5

Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation models for specific medical scena…

cs.CV2025

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning

Fei Zhang, Tianfei Zhou, Jiangchao Yao +3

Prompt tuning (PT), as an emerging resource-efficient fine-tuning paradigm, has showcased remarkable effectiveness in improving the task-specific transferability of vision-language…

cs.CV2025

Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning

Zihua Zhao, Feng Hong, Mengxi Chen +5

The remarkable success of contrastive-learning-based multimodal models has been greatly driven by training on ever-larger datasets with expensive compute consumption. Sample select…

cs.CV2025

Learning to Instruct for Visual Instruction Tuning

Zhihan Zhou, Feng Hong, Jiaan Luo +5

We propose L2T, an advancement of visual instruction tuning (VIT). While VIT equips Multimodal LLMs (MLLMs) with promising multimodal capabilities, the current design choices for V…

cs.CV2024

Self-supervised Anomaly Detection Pretraining Enhances Long-tail ECG Diagnosis

Aofan Jiang, Chaoqin Huang, Qing Cao +5

Current computer-aided ECG diagnostic systems struggle with the underdetection of rare but critical cardiac anomalies due to the imbalanced nature of ECG datasets. This study intro…

cs.CV2024★ 1 cited

Few-Shot Anomaly Detection via Category-Agnostic Registration Learning

Chaoqin Huang, Haoyan Guan, Aofan Jiang +4

Most existing anomaly detection (AD) methods require a dedicated model for each category. Such a paradigm, despite its promising results, is computationally expensive and inefficie…

cs.CV2024★ 2 cited

Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts

Ruipeng Zhang, Ziqing Fan, Jiangchao Yao +2

This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM) algorithm for optimization under domain shifts. It is motivated by the inconsistent convergence degree of…