24 citations · 25 across the 6 of their papers we have counts for
6 papers
Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection
Linhai Zhuo, Junxi Cai, Tianwen Qian +2
Data augmentation, which simulates diverse visual variations to expand the source distribution and induce synthetic domain shifts, is a simple yet effective strategy for mitigating…
3SPO: State-Score-Supervised Policy Optimization for LLM Agents
Yu Han, Kailing Li, Yang Jiao +4
Training large language models (LLMs) as autonomous agents via reinforcement learning (RL) has enabled frontier models to achieve superhuman performance in long-horizon tasks. Howe…
CLIP Based Region-Aware Feature Fusion for Automated BBPS Scoring in Colonoscopy Images
Yujia Fu, Zhiyu Dong, Tianwen Qian +3
Accurate assessment of bowel cleanliness is essential for effective colonoscopy procedures. The Boston Bowel Preparation Scale (BBPS) offers a standardized scoring system but suffe…
NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Yuqian Fu, Xingyu Qiu, Bin Ren +59
Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains. In conjunction…
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting
Linhai Zhuo, Zheng Wang, Yuqian Fu +1
The source-free cross-domain few-shot learning (CD-FSL) task aims to transfer pretrained models to target domains utilizing minimal samples, eliminating the need for source domain…
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning
Linhai Zhuo, Yuqian Fu, Jingjing Chen +2
Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target d…