1 citations · 1 across the 6 of their papers we have counts for
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Large-Scale Universal Defect Generation: Foundation Models and Datasets
Yuanting Fan, Jun Liu, Bin-Bin Gao +5
Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scale paired defect editing data.…
DRL: Discriminative Representation Learning with Parallel Adapters for Class Incremental Learning
Jiawei Zhan, Jun Liu, Jinlong Peng +4
With the excellent representation capabilities of Pre-Trained Models (PTMs), remarkable progress has been made in non-rehearsal Class-Incremental Learning (CIL) research. However,…
BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation
Jinxiang Lai, Wenlong Wu, Jiawei Zhan +5
Box-supervised instance segmentation methods aim to achieve instance segmentation with only box annotations. Recent methods have demonstrated the effectiveness of acquiring high-qu…
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning
Jiawei Zhan, Jinxiang Lai, Bin-Bin Gao +3
Anomaly detection, the technique of identifying abnormal samples using only normal samples, has attracted widespread interest in industry. Existing one-model-per-category methods o…
MatchDet: A Collaborative Framework for Image Matching and Object Detection
Jinxiang Lai, Wenlong Wu, Bin-Bin Gao +5
Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i.e. task-individual). In this pa…