most citedTwo-Level Residual Distillation based Triple Network for Incremental Object Detection

17 citations · 31 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20214 cited

Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection

Xugong Qin, Yu Zhou, Youhui Guo +5

Due to the large success in object detection and instance segmentation, Mask R-CNN attracts great attention and is widely adopted as a strong baseline for arbitrary-shaped scene te…

cs.CV20211 cited

Rescuing Deep Hashing from Dead Bits Problem

Shu Zhao, Dayan Wu, Yucan Zhou +2

Deep hashing methods have shown great retrieval accuracy and efficiency in large-scale image retrieval. How to optimize discrete hash bits is always the focus in deep hashing metho…

cs.CV20203 cited

Exploring Relations in Untrimmed Videos for Self-Supervised Learning

Dezhao Luo, Bo Fang, Yu Zhou +3

Existing video self-supervised learning methods mainly rely on trimmed videos for model training. However, trimmed datasets are manually annotated from untrimmed videos. In this se…

cs.CV202017 cited

Two-Level Residual Distillation based Triple Network for Incremental Object Detection

Dongbao Yang, Yu Zhou, Dayan Wu +3

Modern object detection methods based on convolutional neural network suffer from severe catastrophic forgetting in learning new classes without original data. Due to time consumpt…

cs.CV20206 cited

FC2RN: A Fully Convolutional Corner Refinement Network for Accurate Multi-Oriented Scene Text Detection

Xugong Qin, Yu Zhou, Dayan Wu +2

Recent scene text detection works mainly focus on curve text detection. However, in real applications, the curve texts are more scarce than the multi-oriented ones. Accurate detect…