1 citations · 1 across the 6 of their papers we have counts for
6 papers
Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
Jiabei He, Yang Shen, Xiu-Shen Wei +1
Fine-Grained Image Recognition (FGIR) is a fundamental and challenging task in computer vision and multimedia that plays a crucial role in Intellectual Economy and Industrial Inter…
Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval
Xiu-Shen Wei, Yang Shen, Xuhao Sun +2
Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on…
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation
Tengjin Weng, Yang Shen, Kai Jin +5
Automatic segmentation of fluid in Optical Coherence Tomography (OCT) images is beneficial for ophthalmologists to make an accurate diagnosis. Although semi-supervised OCT fluid se…
Equiangular Basis Vectors
Yang Shen, Xuhao Sun, Xiu-Shen Wei
We propose Equiangular Basis Vectors (EBVs) for classification tasks. In deep neural networks, models usually end with a k-way fully connected layer with softmax to handle differen…
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition
Xiu-Shen Wei, Xuhao Sun, Yang Shen +3
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised dis…
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
Yang Shen, Xuhao Sun, Xiu-Shen Wei +2
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-g…