4.2k citations · 4.6k across the 24 of their papers we have counts for
24 papers
Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering
Wei Feng, Lie Ju, Lin Wang +2
Existing deep learning models have achieved promising performance in recognizing skin diseases from dermoscopic images. However, these models can only recognize samples from predef…
EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos
Ruyi Zha, Xuelian Cheng, Hongdong Li +2
Reconstructing soft tissues from stereo endoscope videos is an essential prerequisite for many medical applications. Previous methods struggle to produce high-quality geometry and…
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
Peng Xia, Di Xu, Ming Hu +2
Long-tailed multi-label visual recognition (LTML) task is a highly challenging task due to the label co-occurrence and imbalanced data distribution. In this work, we propose a unif…
TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification
Litao Yang, Deval Mehta, Sidong Liu +3
Digital pathology based on whole slide images (WSIs) plays a key role in cancer diagnosis and clinical practice. Due to the high resolution of the WSI and the unavailability of pat…
Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation
Litao Yang, Deval Mehta, Dwarikanath Mahapatra +1
Recently, a lot of automated white blood cells (WBC) or leukocyte classification techniques have been developed. However, all of these methods only utilize a single modality micros…
T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up
Deyin Liu, Lin Yuanbo Wu, Bo Li +1
In this paper, we present an end-to-end approach to generate high-resolution person images conditioned on texts only. State-of-the-art text-to-image generation models are mainly de…
Deep Laparoscopic Stereo Matching with Transformers
Xuelian Cheng, Yiran Zhong, Mehrtash Harandi +3
The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection.…
Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification
Lin Wu, Deyin Liu, Wenying Zhang +5
Person re-identification (re-ID) is of great importance to video surveillance systems by estimating the similarity between a pair of cross-camera person shorts. Current methods for…
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images
Deval Mehta, Yaniv Gal, Adrian Bowling +2
Recent years have witnessed a rapid development of automated methods for skin lesion diagnosis and classification. Due to an increasing deployment of such systems in clinics, it ha…
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…
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
Yicheng Wu, Zhonghua Wu, Qianyi Wu +2
Semi-supervised segmentation remains challenging in medical imaging since the amount of annotated medical data is often scarce and there are many blurred pixels near the adhesive e…
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…
ZSTAD: Zero-Shot Temporal Activity Detection
Lingling Zhang, Xiaojun Chang, Jun Liu +4
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Curren…
Generative OpenMax for Multi-Class Open Set Classification
ZongYuan Ge, Sergey Demyanov, Zetao Chen +1
We present a conceptually new and flexible method for multi-class open set classification. Unlike previous methods where unknown classes are inferred with respect to the feature or…
Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification
Lin Wu, Yang Wang
Given a pedestrian image as a query, the purpose of person re-identification is to identify the correct match from a large collection of gallery images depicting the same person ca…
Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification
ZongYuan Ge, Chris McCool, Conrad Sanderson +4
Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to i…
Simple Online and Realtime Tracking
Alex Bewley, Zongyuan Ge, Lionel Ott +2
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, det…
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
ZongYuan Ge, Alex Bewley, Christopher McCool +3
We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification…
Subset Feature Learning for Fine-Grained Category Classification
Zongyuan Ge, Christopher Mccool, Conrad Sanderson +1
Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning…
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
ZongYuan Ge, Chris McCool, Conrad Sanderson +1
We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have co…