26 citations · 26 across the 6 of their papers we have counts for
5 papers · 1 filter
The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Xingyu Qiu, Yuqian Fu, Jiawei Geng +70
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across…
A Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helps
Xuanlong Yu, Youyang Sha, Longfei Liu +2
Few-shot object detection (FSOD) is challenging due to unstable optimization and limited generalization arising from the scarcity of training samples. To address these issues, we p…
From Misclassifications to Outliers: Joint Reliability Assessment in Classification
Yang Li, Youyang Sha, Yinzhi Wang +4
Building reliable classifiers is a fundamental challenge for deploying machine learning in real-world applications. A reliable system should not only detect out-of-distribution (OO…
EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation
Longfei Liu, Yongjie Hou, Yang Li +7
Deploying high-performance dense prediction models on resource-constrained edge devices remains challenging due to strict computation and memory budgets. In practice, lightweight s…
FSOD-VFM: Few-Shot Object Detection with Vision Foundation Models and Graph Diffusion
Chen-Bin Feng, Youyang Sha, Longfei Liu +4
In this paper, we present FSOD-VFM: Few-Shot Object Detectors with Vision Foundation Models, a framework that leverages vision foundation models to tackle the challenge of few-shot…