activity
20182021
most citedMulti-Objective Matrix Normalization for Fine-grained Visual Recognition

100 citations · 127 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV20215 cited

Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning

Shaobo Min, Qi Dai, Hongtao Xie +3

Cross-modal correlation provides an inherent supervision for video unsupervised representation learning. Existing methods focus on distinguishing different video clips by visual an…

cs.CV20212 cited

Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning

Chaoqun Wang, Xuejin Chen, Shaobo Min +2

Generalized Zero-Shot Learning (GZSL) targets recognizing new categories by learning transferable image representations. Existing methods find that, by aligning image representatio…

cs.CV20201 cited

Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning

Hantao Yao, Shaobo Min, Yongdong Zhang +1

Transductive Zero-shot learning (ZSL) targets to recognize the unseen categories by aligning the visual and semantic information in a joint embedding space. There exist four kinds…

cs.CV2020100 cited

Multi-Objective Matrix Normalization for Fine-grained Visual Recognition

Shaobo Min, Hantao Yao, Hongtao Xie +2

Bilinear pooling achieves great success in fine-grained visual recognition (FGVC). Recent methods have shown that the matrix power normalization can stabilize the second-order info…

cs.CV202019 cited

Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning

Shaobo Min, Hantao Yao, Hongtao Xie +3

Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual repre…

cs.CV2019

Domain-Specific Embedding Network for Zero-Shot Recognition

Shaobo Min, Hantao Yao, Hongtao Xie +2

Zero-Shot Learning (ZSL) seeks to recognize a sample from either seen or unseen domain by projecting the image data and semantic labels into a joint embedding space. However, most…