786 citations · 7.3k across the 175 of their papers we have counts for
182 papers
WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields
Muyu Xu, Fangneng Zhan, Jiahui Zhang +5
Neural Radiance Field (NeRF) has shown impressive performance in novel view synthesis via implicit scene representation. However, it usually suffers from poor scalability as requir…
A Survey of Label-Efficient Deep Learning for 3D Point Clouds
Aoran Xiao, Xiaoqin Zhang, Ling Shao +1
In the past decade, deep neural networks have achieved significant progress in point cloud learning. However, collecting large-scale precisely-annotated training data is extremely…
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…
GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference
Peng Tu, Yawen Huang, Feng Zheng +3
Semi-supervised learning is a challenging problem which aims to construct a model by learning from limited labeled examples. Numerous methods for this task focus on utilizing the p…
Generative Kernel Continual learning
Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao +1
Kernel continual learning by \citet{derakhshani2021kernel} has recently emerged as a strong continual learner due to its non-parametric ability to tackle task interference and cata…
TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning
Shiming Chen, Ziming Hong, Wenjin Hou +6
Zero-shot learning (ZSL) tackles the novel class recognition problem by transferring semantic knowledge from seen classes to unseen ones. Existing attention-based models have strug…
Hierarchical Variational Memory for Few-shot Learning Across Domains
Yingjun Du, Xiantong Zhen, Ling Shao +1
Neural memory enables fast adaptation to new tasks with just a few training samples. Existing memory models store features only from the single last layer, which does not generaliz…
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
Shiming Chen, Guo-Sen Xie, Yang Liu +5
Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge…
Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance
Nian Liu, Wangbo Zhao, Dingwen Zhang +2
The application of light field data in salient object de-tection is becoming increasingly popular recently. The diffi-culty lies in how to effectively fuse the features within the…
Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection
Ni Zhang, Junwei Han, Nian Liu +1
Humans perform co-saliency detection by first summarizing the consensus knowledge in the whole group and then searching corresponding objects in each image. Previous methods usuall…
Full-Duplex Strategy for Video Object Segmentation
Ge-Peng Ji, Deng-Ping Fan, Keren Fu +3
Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across thes…
Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation
Hongjun Chen, Jinbao Wang, Hong Cai Chen +4
Annotation burden has become one of the biggest barriers to semantic segmentation. Approaches based on click-level annotations have therefore attracted increasing attention due to…
Discriminative Region-based Multi-Label Zero-Shot Learning
Sanath Narayan, Akshita Gupta, Salman Khan +3
Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL since several objects can co-exist in a natural image. However, the occurrence of…