no code implementations • 1 Mar 2024 • Athanasios Tragakis, Qianying Liu, Chaitanya Kaul, Swalpa Kumar Roy, Hang Dai, Fani Deligianni, Roderick Murray-Smith, Daniele Faccio
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance.
no code implementations • 13 Jan 2024 • Yu Hong, Qian Liu, Huayuan Cheng, Danjiao Ma, Hang Dai, Yu Wang, Guangzhi Cao, Yong Ding
The past few years have witnessed the rapid development of vision-centric 3D perception in autonomous driving.
1 code implementation • CVPR 2023 • Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, Huan Xiong
Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection.
1 code implementation • CVPR 2023 • Jiale Li, Hang Dai, Hao Han, Yong Ding
We propose a multi-modal 3D semantic segmentation model (MSeg3D) with joint intra-modal feature extraction and inter-modal feature fusion to mitigate the modality heterogeneity.
no code implementations • 4 Feb 2023 • Nick Pears, Hang Dai, Will Smith, Hao Sun
We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP).
1 code implementation • 14 Nov 2022 • Yu Hong, Hang Dai, Yong Ding
Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e. g., Pseudo-LiDAR methods.
Ranked #1 on Monocular 3D Object Detection on KITTI Cyclist Hard (using extra training data)
no code implementations • 8 Aug 2022 • Yunqing Bao, Hang Dai, Abdulmotaleb Elsaddik
Salient Object Detection (SOD) is a popular and important topic aimed at precise detection and segmentation of the interesting regions in the images.
1 code implementation • 22 Mar 2022 • Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Ying Tai, Chengjie Wang, Ling Shao
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings.
1 code implementation • 6 Mar 2022 • Xuebin Qin, Hang Dai, Xiaobin Hu, Deng-Ping Fan, Ling Shao, and Luc Van Gool
We present a systematic study on a new task called dichotomous image segmentation (DIS) , which aims to segment highly accurate objects from natural images.
Ranked #5 on Dichotomous Image Segmentation on DIS-TE1
1 code implementation • CVPR 2022 • Yi-Nan Chen, Hang Dai, Yong Ding
Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image.
Ranked #1 on Monocular 3D Object Detection on KITTI Cars Moderate (AP metric)
1 code implementation • 7 Feb 2022 • Zhou Huang, Tian-Zhu Xiang, Huai-Xin Chen, Hang Dai
To this end, in this paper, we propose a novel weakly-supervised salient object detection framework to predict the saliency of remote sensing images from sparse scribble annotations.
no code implementations • 21 Nov 2021 • Chaitanya Kaul, Joshua Mitton, Hang Dai, Roderick Murray-Smith
It achieves this feat due to its effectiveness in creating a novel and robust attention-based point set embedding through a convolutional projection layer crafted for processing dynamically local point set neighbourhoods.
no code implementations • 11 Aug 2021 • Sohail A. Khan, Hang Dai
The comprehensive experiments on various public deepfake datasets demonstrate that the proposed video transformer model with incremental learning achieves state-of-the-art performance in the deepfake video detection task with enhanced feature learning from the sequenced data.
1 code implementation • 8 Aug 2021 • Jiale Li, Hang Dai, Ling Shao, Yong Ding
In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object detector with a voxel-to-point decoder.
2 code implementations • 8 Aug 2021 • Jiale Li, Hang Dai, Ling Shao, Yong Ding
We propose an attentive module to fit the sparse feature maps to dense mostly on the object regions through the deformable convolution tower and the supervised mask-guided attention.
1 code implementation • CVPR 2021 • Shujie Luo, Hang Dai, Ling Shao, Yong Ding
In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores.
no code implementations • 11 Feb 2021 • Sohail Ahmed Khan, Alessandro Artusi, Hang Dai
The proposed technique outperforms state-of-the-art models with 96. 5% accuracy, when tested on publicly available DeepFake Detection Challenge (DFDC) test data, comprising of 400 videos.
no code implementations • 7 Oct 2020 • Hao Sun, Nick Pears, Hang Dai
The ear, as an important part of the human head, has received much less attention compared to the human face in the area of computer vision.
no code implementations • 10 Apr 2020 • Jiale Li, Shujie Luo, Ziqi Zhu, Hang Dai, Andrey S. Krylov, Yong Ding, Ling Shao
In order to obtain a more accurate IoU prediction, we propose a 3D IoU-Net with IoU sensitive feature learning and an IoU alignment operation.
no code implementations • 4 Dec 2019 • Chaitanya Kaul, Nick Pears, Hang Dai, Roderick Murray-Smith, Suresh Manandhar
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation.
no code implementations • 22 Oct 2019 • Chaitanya Kaul, Nick Pears, Hang Dai, Roderick Murray-Smith, Suresh Manandhar
Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth.
no code implementations • ICCV 2019 • Hang Dai, Ling Shao
The data with refined correspondence can be fed to the PointAE again and bootstrap the constructed statistical models.
1 code implementation • 25 Jul 2019 • Zhijie Zhang, Huazhu Fu, Hang Dai, Jianbing Shen, Yanwei Pang, Ling Shao
Segmentation is a fundamental task in medical image analysis.
Ranked #1 on Optic Disc Segmentation on REFUGE
no code implementations • 21 Mar 2018 • Hang Dai, Nick Pears, William Smith
We present a new fully-automatic non-rigid 3D shape registration (morphing) framework comprising (1) a new 3D landmarking and pose normalisation method; (2) an adaptive shape template method to accelerate the convergence of registration algorithms and achieve a better final shape correspondence and (3) a new iterative registration method that combines Iterative Closest Points with Coherent Point Drift (CPD) to achieve a more stable and accurate correspondence establishment than standard CPD.
no code implementations • ICCV 2017 • Hang Dai, Nick Pears, William A. P. Smith, Christian Duncan
We present a fully automatic pipeline to train 3D Morphable Models (3DMMs), with contributions in pose normalisation, dense correspondence using both shape and texture information, and high quality, high resolution texture mapping.
no code implementations • CVPR 2016 • Chao Zhang, William A. P. Smith, Arnaud Dessein, Nick Pears, Hang Dai
In this paper we present a method for computing dense correspondence between a set of 3D face meshes using functional maps.