129 citations · 742 across the 38 of their papers we have counts for
9 papers · 1 filter
Low-Light Image Enhancement with Normalizing Flow
Yufei Wang, Renjie Wan, Wenhan Yang +3
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise r…
Variational Disentanglement for Domain Generalization
Yufei Wang, Haoliang Li, Hao Cheng +3
Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain. In this paper, we propose to tackle the problem of domain generalizatio…
Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration
Jie Chen, Zaifeng Yang, Tsz Nam Chan +3
High Dynamic Range (HDR) imaging via multi-exposure fusion is an important task for most modern imaging platforms. In spite of recent developments in both hardware and algorithm in…
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation
Yufei Wang, Haoliang Li, Lap-pui Chau +1
Though convolutional neural networks are widely used in different tasks, lack of generalization capability in the absence of sufficient and representative data is one of the challe…
Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification
Lisha Tang, Yi Wang, Lap-Pui Chau
Vehicle re-identification (Re-ID) aims to retrieve images with the same vehicle ID across different cameras. Current part-level feature learning methods typically detect vehicle pa…
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack
Alexander Matyasko, Lap-Pui Chau
State-of-the-art deep neural networks are sensitive to small input perturbations. Since the discovery of this intriguing vulnerability, many defence methods have been proposed that…
Multi-object Tracking with Tracked Object Bounding Box Association
Nanyang Yang, Yi Wang, Lap-Pui Chau
The CenterTrack tracking algorithm achieves state-of-the-art tracking performance using a simple detection model and single-frame spatial offsets to localize objects and predict th…
Moving Towards Centers: Re-ranking with Attention and Memory for Re-identification
Yunhao Zhou, Yi Wang, Lap-Pui Chau
Re-ranking utilizes contextual information to optimize the initial ranking list of person or vehicle re-identification (re-ID), which boosts the retrieval performance at post-proce…
Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization
Yi Wang, Xinyu Hou, Lap-Pui Chau
In this paper, we propose a simple yet effective crowd counting and localization network named SCALNet. Unlike most existing works that separate the counting and localization tasks…