29 citations · 65 across the 8 of their papers we have counts for
8 papers
Dual Feature Augmentation Network for Generalized Zero-shot Learning
Lei Xiang, Yuan Zhou, Haoran Duan +1
Zero-shot learning (ZSL) aims to infer novel classes without training samples by transferring knowledge from seen classes. Existing embedding-based approaches for ZSL typically emp…
UniHead: Unifying Multi-Perception for Detection Heads
Hantao Zhou, Rui Yang, Yachao Zhang +5
The detection head constitutes a pivotal component within object detectors, tasked with executing both classification and localization functions. Regrettably, the commonly used par…
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
Xingyu Miao, Yang Bai, Haoran Duan +5
Self-supervised monocular depth estimation methods typically rely on the reprojection error to capture geometric relationships between successive frames in static environments. How…
Knowing the Past to Predict the Future: Reinforcement Virtual Learning
Peng Zhang, Yawen Huang, Bingzhang Hu +5
Reinforcement Learning (RL)-based control system has received considerable attention in recent decades. However, in many real-world problems, such as Batch Process Control, the env…
Absolute Zero-Shot Learning
Rui Gao, Fan Wan, Daniel Organisciak +6
Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i.e., training a classifier with zero r…
Semi-Supervised Crowd Counting from Unlabeled Data
Haoran Duan, Fan Wan, Rui Sun +6
Automatic Crowd behavior analysis can be applied to effectively help the daily transportation statistics and planning, which helps the smart city construction. As one of the most i…
EfficientTDNN: Efficient Architecture Search for Speaker Recognition
Rui Wang, Zhihua Wei, Haoran Duan +3
Convolutional neural networks (CNNs), such as the time-delay neural network (TDNN), have shown their remarkable capability in learning speaker embedding. However, they meanwhile br…
SOFA-Net: Second-Order and First-order Attention Network for Crowd Counting
Haoran Duan, Shidong Wang, Yu Guan
Automated crowd counting from images/videos has attracted more attention in recent years because of its wide application in smart cities. But modelling the dense crowd heads is cha…