80 citations · 249 across the 21 of their papers we have counts for
17 papers · 1 filter
Semantic-aware Consistency Network for Cloth-changing Person Re-Identification
Peini Guo, Hong Liu, Jianbing Wu +2
Cloth-changing Person Re-Identification (CC-ReID) is a challenging task that aims to retrieve the target person across multiple surveillance cameras when clothing changes might hap…
Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
Mengyuan Liu, Hong Liu, Tianyu Guo
Considering the instance-level discriminative ability, contrastive learning methods, including MoCo and SimCLR, have been adapted from the original image representation learning ta…
Sparse-Inductive Generative Adversarial Hashing for Nearest Neighbor Search
Hong Liu
Unsupervised hashing has received extensive research focus on the past decade, which typically aims at preserving a predefined metric (i.e. Euclidean metric) in the Hamming space.…
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,…
Revisiting and Advancing Adversarial Training Through A Simple Baseline
Hong Liu
In this paper, we delve into the essential components of adversarial training which is a pioneering defense technique against adversarial attacks. We indicate that some factors suc…
Latent Feature Relation Consistency for Adversarial Robustness
Xingbin Liu, Huafeng Kuang, Hong Liu +3
Deep neural networks have been applied in many computer vision tasks and achieved state-of-the-art performance. However, misclassification will occur when DNN predicts adversarial…
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…
Facial-Sketch Synthesis: A New Challenge
Deng-Ping Fan, Ziling Huang, Peng Zheng +3
This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high costs of obtaining hand-drawn sketch datasets, there lacks a complete be…
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…
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
Yixu Wang, Jie Li, Hong Liu +4
Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we obse…
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…
Projection & Probability-Driven Black-Box Attack
Jie Li, Rongrong Ji, Hong Liu +4
Generating adversarial examples in a black-box setting retains a significant challenge with vast practical application prospects. In particular, existing black-box attacks suffer f…
Supervised Online Hashing via Hadamard Codebook Learning
Mingbao Lin, Rongrong Ji, Hong Liu +1
In recent years, binary code learning, a.k.a hashing, has received extensive attention in large-scale multimedia retrieval. It aims to encode high-dimensional data points to binary…
Towards Visual Feature Translation
Jie Hu, Rongrong Ji, Hong Liu +3
Most existing visual search systems are deployed based upon fixed kinds of visual features, which prohibits the feature reusing across different systems or when upgrading systems w…
Universal Perturbation Attack Against Image Retrieval
Jie Li, Rongrong Ji, Hong Liu +3
Universal adversarial perturbations (UAPs), a.k.a. input-agnostic perturbations, has been proved to exist and be able to fool cutting-edge deep learning models on most of the data…
CerfGAN: A Compact, Effective, Robust, and Fast Model for Unsupervised Multi-Domain Image-to-Image Translation
Xiao Liu, Shengchuan Zhang, Hong Liu +3
In this paper, we aim at solving the multi-domain image-to-image translation problem with a unified model in an unsupervised manner. The most successful work in this area refers to…
Ordinal Constrained Binary Code Learning for Nearest Neighbor Search
Hong Liu, Rongrong Ji, Yongjian Wu +1
Recent years have witnessed extensive attention in binary code learning, a.k.a. hashing, for nearest neighbor search problems. It has been seen that high-dimensional data points ca…