24 citations · 189 across the 82 of their papers we have counts for
10 papers · 1 filter
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification
Xiaohua Chen, Yucan Zhou, Dayan Wu +4
Real-world data often follows a long-tailed distribution, which makes the performance of existing classification algorithms degrade heavily. A key issue is that samples in tail cat…
TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation
Wei Wang, Yu Zhou, Jiahao Lv +4
The research focus of scene text detection and recognition has shifted to arbitrary shape text in recent years, where the text shape representation is a fundamental problem. An ide…
Dense Semantic Contrast for Self-Supervised Visual Representation Learning
Xiaoni Li, Yu Zhou, Yifei Zhang +5
Self-supervised representation learning for visual pre-training has achieved remarkable success with sample (instance or pixel) discrimination and semantics discovery of instance,…
PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition
Zhi Qiao, Yu Zhou, Jin Wei +5
Nowadays, scene text recognition has attracted more and more attention due to its various applications. Most state-of-the-art methods adopt an encoder-decoder framework with attent…
Which and Where to Focus: A Simple yet Accurate Framework for Arbitrary-Shaped Nearby Text Detection in Scene Images
Youhui Guo, Yu Zhou, Xugong Qin +1
Scene text detection has drawn the close attention of researchers. Though many methods have been proposed for horizontal and oriented texts, previous methods may not perform well w…
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…
Video 3D Sampling for Self-supervised Representation Learning
Wei Li, Dezhao Luo, Bo Fang +2
Most of the existing video self-supervised methods mainly leverage temporal signals of videos, ignoring that the semantics of moving objects and environmental information are all c…
Multi-View Correlation Distillation for Incremental Object Detection
Dongbao Yang, Yu Zhou, Weiping Wang
In real applications, new object classes often emerge after the detection model has been trained on a prepared dataset with fixed classes. Due to the storage burden and the privacy…
MMF: Multi-Task Multi-Structure Fusion for Hierarchical Image Classification
Xiaoni Li, Yucan Zhou, Yu Zhou +1
Hierarchical classification is significant for complex tasks by providing multi-granular predictions and encouraging better mistakes. As the label structure decides its performance…
Exploring Instance Relations for Unsupervised Feature Embedding
Yifei Zhang, Yu Zhou, Weiping Wang
Despite the great progress achieved in unsupervised feature embedding, existing contrastive learning methods typically pursue view-invariant representations through attracting posi…