786 citations · 7.3k across the 175 of their papers we have counts for
12 papers · 1 filter
Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis
Hao Tang, Ling Shao, Philip H. S. Torr +1
We present a novel bipartite graph reasoning Generative Adversarial Network (BiGraphGAN) for two challenging tasks: person pose and facial image synthesis. The proposed graph gener…
Evolutionary Generalized Zero-Shot Learning
Dubing Chen, Chenyi Jiang, Haofeng Zhang
Attribute-based Zero-Shot Learning (ZSL) has revolutionized the ability of models to recognize new classes not seen during training. However, with the advancement of large-scale mo…
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
Xingping Dong, Jianbing Shen, Ling Shao
The pioneering method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervis…
RePFormer: Refinement Pyramid Transformer for Robust Facial Landmark Detection
Jinpeng Li, Haibo Jin, Shengcai Liao +2
This paper presents a Refinement Pyramid Transformer (RePFormer) for robust facial landmark detection. Most facial landmark detectors focus on learning representative image feature…
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning
Xingping Dong, Tianran Ouyang, Shengcai Liao +2
Most existing few-shot learning (FSL) methods require a large amount of labeled data in meta-training, which is a major limit. To reduce the requirement of labels, a semi-supervise…
Learning Non-target Knowledge for Few-shot Semantic Segmentation
Yuanwei Liu, Nian Liu, Qinglong Cao +3
Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in non-target r…
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization
Minghui Chen, Cheng Wen, Feng Zheng +2
Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been t…
Learning Enriched Features for Fast Image Restoration and Enhancement
Syed Waqas Zamir, Aditya Arora, Salman Khan +4
Given a degraded input image, image restoration aims to recover the missing high-quality image content. Numerous applications demand effective image restoration, e.g., computationa…
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
Irtiza Hasan, Shengcai Liao, Jinpeng Li +2
Pedestrian detection is the cornerstone of many vision based applications, starting from object tracking to video surveillance and more recently, autonomous driving. With the rapid…
Highly Accurate Dichotomous Image Segmentation
Xuebin Qin, Hang Dai, Xiaobin Hu +3
We present a systematic study on a new task called dichotomous image segmentation (DIS) , which aims to segment highly accurate objects from natural images. To this end, we collect…
Local and Global GANs with Semantic-Aware Upsampling for Image Generation
Hao Tang, Ling Shao, Philip H. S. Torr +1
In this paper, we address the task of semantic-guided image generation. One challenge common to most existing image-level generation methods is the difficulty in generating small o…
Learning to Generalize across Domains on Single Test Samples
Zehao Xiao, Xiantong Zhen, Ling Shao +1
We strive to learn a model from a set of source domains that generalizes well to unseen target domains. The main challenge in such a domain generalization scenario is the unavailab…