64 citations · 99 across the 9 of their papers we have counts for
14 papers
STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection
Zhenglin Zhou, Huaxia Li, Hong Liu +3
Recently, deep learning-based facial landmark detection has achieved significant improvement. However, the semantic ambiguity problem degrades detection performance. Specifically,…
Self-distillation with Online Diffusion on Batch Manifolds Improves Deep Metric Learning
Zelong Zeng, Fan Yang, Hong Liu +1
Recent deep metric learning (DML) methods typically leverage solely class labels to keep positive samples far away from negative ones. However, this type of method normally ignores…
Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification
Jianbing Wu, Hong Liu, Wei Shi +2
Cloth-changing person re-identification (CC-ReID), which aims to match person identities under clothing changes, is a new rising research topic in recent years. However, typical bi…
Improving Camouflaged Object Detection with the Uncertainty of Pseudo-edge Labels
Nobukatsu Kajiura, Hong Liu, Shin'ichi Satoh
This paper focuses on camouflaged object detection (COD), which is a task to detect objects hidden in the background. Most of the current COD models aim to highlight the target obj…
Towards Robustness Against Natural Language Word Substitutions
Xinshuai Dong, Anh Tuan Luu, Rongrong Ji +1
Robustness against word substitutions has a well-defined and widely acceptable form, i.e., using semantically similar words as substitutions, and thus it is considered as a fundame…
Anti-Bandit Neural Architecture Search for Model Defense
Hanlin Chen, Baochang Zhang, Song Xue +4
Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks. In this paper, we defend against…