63 citations · 65 across the 6 of their papers we have counts for
6 papers
Image Classification with Deep Reinforcement Active Learning
Mingyuan Jiu, Xuguang Song, Hichem Sahbi +5
Deep learning is currently reaching outstanding performances on different tasks, including image classification, especially when using large neural networks. The success of these m…
Few-Shot Object Detection with Sparse Context Transformers
Jie Mei, Mingyuan Jiu, Hichem Sahbi +2
Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is tran…
Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects
Feng Yan, Xiaoheng Jiang, Yang Lu +5
Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection…
CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation
Xiaoheng Jiang, Kaiyi Guo, Yang Lu +5
Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress,…
Decision Fusion Network with Perception Fine-tuning for Defect Classification
Xiaoheng Jiang, Shilong Tian, Zhiwen Zhu +5
Surface defect inspection is an important task in industrial inspection. Deep learning-based methods have demonstrated promising performance in this domain. Nevertheless, these met…
MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects
Lisha Cui, Rui Ma, Pei Lv +4
For most of the object detectors based on multi-scale feature maps, the shallow layers are rich in fine spatial information and thus mainly responsible for small object detection.…