139 citations · 1.4k across the 81 of their papers we have counts for
137 papers
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…
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…