230 citations · 508 across the 30 of their papers we have counts for
26 papers · 1 filter
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…
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…
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…
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…
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…
Defending Adversarial Examples via DNN Bottleneck Reinforcement
Wenqing Liu, Miaojing Shi, Teddy Furon +1
This paper presents a DNN bottleneck reinforcement scheme to alleviate the vulnerability of Deep Neural Networks (DNN) against adversarial attacks. Typical DNN classifiers encode t…
Active Crowd Counting with Limited Supervision
Zhen Zhao, Miaojing Shi, Xiaoxiao Zhao +1
To learn a reliable people counter from crowd images, head center annotations are normally required. Annotating head centers is however a laborious and tedious process in dense cro…
Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images
Zhaohui Yang, Miaojing Shi, Chao Xu +2
Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire trainin…
Point in, Box out: Beyond Counting Persons in Crowds
Yuting Liu, Miaojing Shi, Qijun Zhao +1
Modern crowd counting methods usually employ deep neural networks (DNN) to estimate crowd counts via density regression. Despite their significant improvements, the regression-base…
Revisiting Perspective Information for Efficient Crowd Counting
Miaojing Shi, Zhaohui Yang, Chao Xu +1
Crowd counting is the task of estimating people numbers in crowd images. Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regres…
Crowd counting via scale-adaptive convolutional neural network
Lu Zhang, Miaojing Shi, Qiaobo Chen
The task of crowd counting is to automatically estimate the pedestrian number in crowd images. To cope with the scale and perspective changes that commonly exist in crowd images, s…