most citedSEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition

20 citations · 39 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20216 cited

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…

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.CV20209 cited

Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification

Yucan Zhou, Yu Wang, Jianfei Cai +3

Deep neural networks are highly effective when a large number of labeled samples are available but fail with few-shot classification tasks. Recently, meta-learning methods have rec…

cs.CV202020 cited

SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition

Zhi Qiao, Yu Zhou, Dongbao Yang +2

Scene text recognition is a hot research topic in computer vision. Recently, many recognition methods based on the encoder-decoder framework have been proposed, and they can handle…