230 citations · 344 across the 9 of their papers we have counts for
10 papers
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization
Wei Dong, Junsheng Wu, Yi Luo +2
The key towards learning informative node representations in graphs lies in how to gain contextual information from the neighbourhood. In this work, we present a simple-yet-effecti…
Implicit Motion Handling for Video Camouflaged Object Detection
Xuelian Cheng, Huan Xiong, Deng-Ping Fan +4
We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from…
Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding
Sourya Dipta Das, Saikat Dutta, Nisarg A. Shah +2
Convolutional Neural Network models have successfully detected retinal illness from optical coherence tomography (OCT) and fundus images. These CNN models frequently rely on vast a…
Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling
Lie Ju, Xin Wang, Xin Zhao +3
Recently, ultra-widefield (UWF) 200\degree~fundus imaging by Optos cameras has gradually been introduced because of its broader insights for detecting more information on the fundu…
Hierarchical Neural Architecture Search for Deep Stereo Matching
Xuelian Cheng, Yiran Zhong, Mehrtash Harandi +5
To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classific…
Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation
Lie Ju, Xin Wang, Quan Zhou +5
For decades, advances in retinal imaging technology have enabled effective diagnosis and management of retinal disease using fundus cameras. Recently, ultra-wide-field (UWF) fundus…