2.3k citations · 4.1k across the 41 of their papers we have counts for
33 papers · 1 filter
ExpNet: A unified network for Expert-Level Classification
Junde Wu, Huihui Fang, Yehui Yang +4
Different from the general visual classification, some classification tasks are more challenging as they need the professional categories of the images. In the paper, we call them…
Global-and-Local Collaborative Learning for Co-Salient Object Detection
Runmin Cong, Ning Yang, Chongyi Li +4
The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effec…
RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
Xiaoxiao Liang, Yiqun Lin, Huazhu Fu +2
Federated semi-supervised learning (FSSL) aims to derive a global model by training fully-labeled and fully-unlabeled clients or training partially labeled clients. The existing ap…
Consistency and Diversity induced Human Motion Segmentation
Tao Zhou, Huazhu Fu, Chen Gong +4
Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster d…
VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
Yujun Zhang, Lei Zhu, Wei Feng +5
Lane detection plays a key role in autonomous driving. While car cameras always take streaming videos on the way, current lane detection works mainly focus on individual images (fr…
From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data
Ye Liu, Lei Zhu, Shunda Pei +5
Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing metho…