activity
20242026
most citedLightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation

2 citations · 3 across the 19 of their papers we have counts for

collaborators

23 papers

cs.CV2026

Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

Shilin Yan, Jintao Tong, Hongwei Xue +6

The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit…

cs.CV2026

Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment

Yaze Zhao, Yixiong Zou, Yuhua Li +1

Cross-Domain Few-Shot Learning (CDFSL) adapts models trained with large-scale general data (source domain) to downstream target domains with only scarce training data, where the re…

cs.CV2026

Remedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection

Yongwei Jiang, Yixiong Zou, Yuhua Li +1

Cross-domain few-shot object detection (CD-FSOD) aims to adapt pretrained detectors from a source domain to target domains with limited annotations, suffering from severe domain sh…

cs.CV2026

Mind the Discriminability Trap in Source-Free Cross-domain Few-shot Learning

Zhenyu Zhang, Yixiong Zou, Yuhua Li +2

Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where Vision-Langua…

cs.AI2026

Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning

Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2

Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where CLIP has rece…

cs.LG2026

Rethinking Graph Generalization through the Lens of Sharpness-Aware Minimization

Yang Qiu, Yixiong Zou, Jun Wang

Graph Neural Networks (GNNs) have achieved remarkable success across various graph-based tasks but remain highly sensitive to distribution shifts. In this work, we focus on a preva…