activity
20182021
most citedSupport Vector Guided Softmax Loss for Face Recognition

44 citations · 134 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV20213 cited

Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations

Tao Wei, Angelica I Aviles-Rivero, Shuo Wang +4

The task of classifying mammograms is very challenging because the lesion is usually small in the high resolution image. The current state-of-the-art approaches for medical image c…

cs.CV202012 cited

A Multimodal Late Fusion Model for E-Commerce Product Classification

Ye Bi, Shuo Wang, Zhongrui Fan

The cataloging of product listings is a fundamental problem for most e-commerce platforms. Despite promising results obtained by unimodal-based methods, it can be expected that the…

cs.IR20201 cited

A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter

Ye Bi, Shuo Wang, Zhongrui Fan

The animated Graphical Interchange Format (GIF) images have been widely used on social media as an intuitive way of expression emotion. Given their expressiveness, GIFs offer a mor…

cs.CV202029 cited

Loss Function Search for Face Recognition

Xiaobo Wang, Shuo Wang, Cheng Chi +2

In face recognition, designing margin-based (e.g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.…

cs.CV201913 cited

Mis-classified Vector Guided Softmax Loss for Face Recognition

Xiaobo Wang, Shifeng Zhang, Shuo Wang +3

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discri…

cs.CV201932 cited

Improved Selective Refinement Network for Face Detection

Shifeng Zhang, Rui Zhu, Xiaobo Wang +5

As a long-standing problem in computer vision, face detection has attracted much attention in recent decades for its practical applications. With the availability of face detection…