research
∙
08/25/2023
An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation
In medical imaging, artificial intelligence (AI) is increasingly being u...
share
research
∙
08/09/2023
SegMatch: A semi-supervised learning method for surgical instrument segmentation
Surgical instrument segmentation is recognised as a key enabler to provi...
research
∙
07/25/2023
When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review
Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously wh...
research
∙
07/12/2023
TreeFormer: a Semi-Supervised Transformer-based Framework for Tree Counting from a Single High Resolution Image
Automatic tree density estimation and counting using single aerial and s...
research
∙
06/20/2023
Automated Grading and Feedback Tools for Programming Education: A Systematic Review
We conducted a systematic literature review on automated grading and fee...
research
∙
06/15/2023
Text Promptable Surgical Instrument Segmentation with Vision-Language Models
In this paper, we propose a novel text promptable surgical instrument se...
research
∙
06/14/2023
LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation
Referring video object segmentation (RVOS) aims to segment the target in...
research
∙
03/28/2023
HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation
Panoptic Scene Graph generation (PSG) is a recently proposed task in ima...
research
∙
12/05/2022
Domain-general Crowd Counting in Unseen Scenarios
Domain shift across crowd data severely hinders crowd counting models to...
research
∙
12/04/2022
Exploring and Eliciting Needs and Preferences from Editors for Wikidata Recommendations
Wikidata is an open knowledge graph created, managed, and maintained col...
research
∙
11/18/2022
α DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling
Differentiable architecture search (DARTS) has been a mainstream directi...
research
∙
10/27/2022
Video-based Remote Physiological Measurement via Self-supervised Learning
Video-based remote physiological measurement aims to estimate remote pho...
research
∙
09/04/2022
A systematic study of race and sex bias in CNN-based cardiac MR segmentation
In computer vision there has been significant research interest in asses...
research
∙
08/04/2022
Redesigning Multi-Scale Neural Network for Crowd Counting
Perspective distortions and crowd variations make crowd counting a chall...
research
∙
07/18/2022
Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interaction
The target of space-time video super-resolution (STVSR) is to increase b...
research
∙
07/12/2022
Dam reservoir extraction from remote sensing imagery using tailored metric learning strategies
Dam reservoirs play an important role in meeting sustainable development...
research
∙
07/08/2022
Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations
Zero-shot learning (ZSL) aims to recognize classes that do not have samp...
research
∙
03/28/2022
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
Recently, vision-language pre-training shows great potential in open-voc...
research
∙
10/30/2021
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning
In visual recognition tasks, few-shot learning requires the ability to l...
research
∙
07/13/2021
Learning to Recommend Items to Wikidata Editors
Wikidata is an open knowledge graph built by a global community of volun...
research
∙
11/10/2020
Detecting Human-Object Interaction with Mixed Supervision
Human object interaction (HOI) detection is an important task in image u...
research
∙
11/09/2020
Fast Fourier Intrinsic Network
We address the problem of decomposing an image into albedo and shading. ...
research
∙
10/22/2020
Restoring Negative Information in Few-Shot Object Detection
Few-shot learning has recently emerged as a new challenge in the deep le...
research
∙
08/12/2020
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
Unsupervised crowd counting is a challenging yet not largely explored ta...
research
∙
08/12/2020
Defending Adversarial Examples via DNN Bottleneck Reinforcement
This paper presents a DNN bottleneck reinforcement scheme to alleviate t...
research
∙
07/13/2020
Active Crowd Counting with Limited Supervision
To learn a reliable people counter from crowd images, head center annota...
research
∙
12/01/2019
Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images
Weakly-supervised object detection attempts to limit the amount of super...
research
∙
04/02/2019
Point in, Box out: Beyond Counting Persons in Crowds
Modern crowd counting methods usually employ deep neural networks (DNN) ...
research
∙
07/05/2018
Perspective-Aware CNN For Crowd Counting
Crowd counting is the task of estimating pedestrian numbers in crowd ima...
research
∙
11/13/2017
Crowd counting via scale-adaptive convolutional neural network
The task of crowd counting is to automatically estimate the pedestrian n...
research
∙
03/23/2017
Weakly Supervised Object Localization Using Things and Stuff Transfer
We propose to help weakly supervised object localization for classes whe...
research
∙
08/15/2016
