230 citations · 508 across the 30 of their papers we have counts for
32 papers
An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation
Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink +3
In medical imaging, artificial intelligence (AI) is increasingly being used to automate routine tasks. However, these algorithms can exhibit and exacerbate biases which lead to dis…
SegMatch: A semi-supervised learning method for surgical instrument segmentation
Meng Wei, Charlie Budd, Luis C. Garcia-Peraza-Herrera +3
Surgical instrument segmentation is recognised as a key enabler in providing advanced surgical assistance and improving computer-assisted interventions. In this work, we propose Se…
TreeFormer: a Semi-Supervised Transformer-based Framework for Tree Counting from a Single High Resolution Image
Hamed Amini Amirkolaee, Miaojing Shi, Mark Mulligan
Automatic tree density estimation and counting using single aerial and satellite images is a challenging task in photogrammetry and remote sensing, yet has an important role in for…
When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review
Maxime Fontana, Michael Spratling, Miaojing Shi
Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple ou…
Automated Grading and Feedback Tools for Programming Education: A Systematic Review
Marcus Messer, Neil C. C. Brown, Michael Kölling +1
We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categ…
Text Promptable Surgical Instrument Segmentation with Vision-Language Models
Zijian Zhou, Oluwatosin Alabi, Meng Wei +2
In this paper, we propose a novel text promptable surgical instrument segmentation approach to overcome challenges associated with diversity and differentiation of surgical instrum…
LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation
Linfeng Yuan, Miaojing Shi, Zijie Yue +1
Referring video object segmentation (RVOS) aims to segment the target instance referred by a given text expression in a video clip. The text expression normally contains sophistica…
HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation
Zijian Zhou, Miaojing Shi, Holger Caesar
Panoptic Scene Graph generation (PSG) is a recently proposed task in image scene understanding that aims to segment the image and extract triplets of subjects, objects and their re…
Exploring and Eliciting Needs and Preferences from Editors for Wikidata Recommendations
Kholoud Alghamdi, Miaojing Shi, Elena Simperl
Wikidata is an open knowledge graph created, managed, and maintained collaboratively by a global community of volunteers. As it continues to grow, it faces substantial editor engag…
Domain-General Crowd Counting in Unseen Scenarios
Zhipeng Du, Jiankang Deng, Miaojing Shi
Domain shift across crowd data severely hinders crowd counting models to generalize to unseen scenarios. Although domain adaptive crowd counting approaches close this gap to a cert…
DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling
Bicheng Guo, Shuxuan Guo, Miaojing Shi +4
Differentiable architecture search (DARTS) has been a mainstream direction in automatic machine learning. Since the discovery that original DARTS will inevitably converge to poor a…
Facial Video-based Remote Physiological Measurement via Self-supervised Learning
Zijie Yue, Miaojing Shi, Shuai Ding
Facial video-based remote physiological measurement aims to estimate remote photoplethysmography (rPPG) signals from human face videos and then measure multiple vital signs (e.g. h…
A systematic study of race and sex bias in CNN-based cardiac MR segmentation
Tiarna Lee, Esther Puyol-Anton, Bram Ruijsink +2
In computer vision there has been significant research interest in assessing potential demographic bias in deep learning models. One of the main causes of such bias is imbalance in…
Redesigning Multi-Scale Neural Network for Crowd Counting
Zhipeng Du, Miaojing Shi, Jiankang Deng +1
Perspective distortions and crowd variations make crowd counting a challenging task in computer vision. To tackle it, many previous works have used multi-scale architecture in deep…
Dam reservoir extraction from remote sensing imagery using tailored metric learning strategies
Arnout van Soesbergen, Zedong Chu, Miaojing Shi +1
Dam reservoirs play an important role in meeting sustainable development goals and global climate targets. However, particularly for small dam reservoirs, there is a lack of consis…
Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interaction
Zijie Yue, Miaojing Shi
The target of space-time video super-resolution (STVSR) is to increase both the frame rate (also referred to as the temporal resolution) and the spatial resolution of a given video…
Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations
Yu Du, Miaojing Shi, Fangyun Wei +1
Zero-shot learning (ZSL) aims to recognize classes that do not have samples in the training set. One representative solution is to directly learn an embedding function associating…
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
Yu Du, Fangyun Wei, Zihe Zhang +3
Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The…
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning
Miao Zhang, Miaojing Shi, Li Li
In visual recognition tasks, few-shot learning requires the ability to learn object categories with few support examples. Its re-popularity in light of the deep learning developmen…
Learning to Recommend Items to Wikidata Editors
Kholoud AlGhamdi, Miaojing Shi, Elena Simperl
Wikidata is an open knowledge graph built by a global community of volunteers. As it advances in scale, it faces substantial challenges around editor engagement. These challenges a…
Detecting Human-Object Interaction with Mixed Supervision
Suresh Kirthi Kumaraswamy, Miaojing Shi, Ewa Kijak
Human object interaction (HOI) detection is an important task in image understanding and reasoning. It is in a form of HOI triplet <human; verb; object>, requiring bounding boxes f…
Fast Fourier Intrinsic Network
Yanlin Qian, Miaojing Shi, Joni-Kristian Kämäräinen +1
We address the problem of decomposing an image into albedo and shading. We propose the Fast Fourier Intrinsic Network, FFI-Net in short, that operates in the spectral domain, split…
Restoring Negative Information in Few-Shot Object Detection
Yukuan Yang, Fangyun Wei, Miaojing Shi +1
Few-shot learning has recently emerged as a new challenge in the deep learning field: unlike conventional methods that train the deep neural networks (DNNs) with a large number of…
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
Yuting Liu, Zheng Wang, Miaojing Shi +3
Unsupervised crowd counting is a challenging yet not largely explored task. In this paper, we explore it in a transfer learning setting where we learn to detect and count persons i…