SelectedWorks - Hang Dai

Selected Works of Hang Dai

Assistant Professor, Department of Computer Vision, MBZUAI

Dr. Hang Dai was an Assistant Professor in the Computer Vision Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).

Biography

Dr. Hang Dai worked as a Research Scientist at the Inception Institute of Artificial Intelligence (IIAI) in Abu Dhabi prior joining MBZUAI. He received his PhD degree from the Department of Computer Science at The University of York, U.K., in 2018. He was an Oversea Research Scholar with the University of York from 2015 to 2018. His research was funded by NHS, Google, Royal Academy of Engineering and the Leverhulme Trust and QIDIS from the National Commissioning Group. He served as a reviewer of ICCV, CVPR, ECCV and program committee member of AAAI. His research interests include geometric deep learning, deepfake detection, 3D computer vision.

Works
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About Hang Dai

Dr. Hang Dai was an Assistant Professor in the Computer Vision Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).

Biography

Dr. Hang Dai worked as a Research Scientist at the Inception Institute of Artificial Intelligence (IIAI) in Abu Dhabi prior joining MBZUAI. He received his PhD degree from the Department of Computer Science at The University of York, U.K., in 2018. He was an Oversea Research Scholar with the University of York from 2015 to 2018. His research was funded by NHS, Google, Royal Academy of Engineering and the Leverhulme Trust and QIDIS from the National Commissioning Group. He served as a reviewer of ICCV, CVPR, ECCV and program committee member of AAAI. His research interests include geometric deep learning, deepfake detection, 3D computer vision.

2020 - 2022	Assistant Professor, Mohamed bin Zayed University of Artificial Intelligence ‐ Department of Computer Vision
Institution Organization Position Type Position Title Time Period to Options Current Position Affiliate my profile with this Institution

Computer Sciences, Artificial Intelligence and Robotics, Statistics and Probability, and Data Science

geometric deep learning, deepfake detection, and 3D computer vision

http://orcid.org/0000-0002-7609-0124

Journal Articles (4)

Applications of 3D photography in craniofacial surgery (Christian Duncan, Nick E. Pears, Hang Dai, et al.) Journal of Pediatric Neurosciences (2022)

Three-dimensional (3D) photography is becoming more common in craniosynostosis practice and may be used for research, archiving, and as a planning tool. In this article, an overview of the uses of 3D photography will be ...

Scribble-based boundary-aware network for weakly supervised salient object detection in remote sensing images (Zhou Huang, Tian-Zhu Xiang, Huai-Xin Chen, et al.) ISPRS Journal of Photogrammetry and Remote Sensing (2022)

Existing CNN-based salient object detection (SOD) models rely heavily on large-scale pixel-level annotations, which are labor-intensive, time-consuming, and expensive. In contrast, sparse annotations (e.g., image-level or scribble) are gradually entering the SOD community. However, few ...

Automatic schelling points detection from meshes (Geng Chen, Hang Dai, Tao Zhou, et al.) IEEE Transactions on Visualization and Computer Graphics (2022)

Mesh Schelling points explain how humans focus on specific regions of a 3D object. They have a large number of important applications in computer graphics and provide valuable information for perceptual psychology studies. However, detecting ...

P2V-RCNN: point to voxel feature learning for 3D object detection from point clouds (Jiale Li, Yu Sun, Shujie Luo, et al.) IEEE Access (2021)

The most recent 3D object detectors for point clouds rely on the coarse voxel-based representation rather than the accurate point-based representation due to a higher box recall in the voxel-based Region Proposal Network (RPN). However, ...

Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection (Yu Hong, Hang Dai and Yong Ding) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2022)

Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e.g., Pseudo-LiDAR methods. However, the existing methods usually apply non-end-to-end training strategies and insufficiently leverage the LiDAR information, ...

UAV-based Multi-scale Features Fusion Attention for Fire Detection in Smart City Ecosystems (Tanveer Hussain, Hang Dai, Wail Gueaieb, et al.) ISC2 2022 - 8th IEEE International Smart Cities Conference (2022)

Effective fire detection using vision sensors is a widely accepted challenge in smart cities and rural areas, where forest and building fires significantly contribute to the loss of human lives and properties. Early fire detection ...

Self-Distillation for Robust LiDAR Semantic Segmentation in Autonomous Driving (Jiale Li, Hang Dai and Yong Ding) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2022)

We propose a new and effective self-distillation framework with our new Test-Time Augmentation (TTA) and Transformer based Voxel Feature Encoder (TransVFE) for robust LiDAR semantic segmentation in autonomous driving, where the robustness is mission-critical but ...

M3DSSD: monocular 3D single stage object detector (Shujie Luo, Hang Dai, Ling Shao, et al.) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2021)

In this paper, we propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. Current anchor-based monocular 3D object detection methods suffer from feature mismatching. To overcome this, we ...

Anchor-free 3D single stage detector with mask-guided attention for point cloud (Jiale Li, Hang Dai, Ling Shao, et al.) MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia (2021)

Most of the existing single-stage and two-stage 3D object detectors are anchor-based methods, while the efficient but challenging anchor-free single-stage 3D object detection is not well investigated. Recent studies on 2D object detection show that ...

From voxel to point: IoU-guided 3D object detection for point cloud with voxel-to-point decoder (Jiale Li, Hang Dai, Ling Shao, et al.) MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia (2021)

In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object detector with a voxel-to-point decoder. To preserve the necessary information from all raw points and maintain the high box recall in voxel based ...

Video transformer for deepfake detection with incremental learning (Sohail Ahmed Khan and Hang Dai) MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia (2021)

Face forgery by deepfake is widely spread over the internet and this raises severe societal concerns. In this paper, we propose a novel video transformer with incremental learning for detecting deepfake videos. To better align ...

Focusnet++: Attentive aggregated transformations for efficient and accurate medical image segmentation (Chaitanya Kaul, Nick Pears, Hang Dai, et al.) Proceedings - International Symposium on Biomedical Imaging (2021)

We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation. We combine attention mechanisms with group convolutions to create our group attention mechanism, which forms the ...

Penalizing small errors using an adaptive logarithmic loss (Chaitanya Kaul, Nick Pears, Hang Dai, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2021)

Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth. Fundamentally, they define a functional landscape for traversal by gradient descent. Although numerous loss functions have been proposed ...

A human ear reconstruction autoencoder (Hao Sun, Nick Pears and Hang Dai) VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2021)

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. Inspired by previous work on monocular 3D face reconstruction ...

C4AV: learning cross-modal representations from transformers (Shujie Luo, Hang Dai, Ling Shao, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2021)

In this paper, we focus on the object referral problem in the autonomous driving setting. We propose a novel framework to learn cross-modal representations from transformers. In order to extract the linguistic feature, we feed ...

Commands for autonomous vehicles by progressively stacking visual-linguistic representations (Hang Dai, Shujie Luo, Yong Ding, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2021)

In this work, we focus on the object referral problem in the autonomous driving setting. We use a stacked visual-linguistic BERT model to learn a generic visual-linguistic representation. Each element of the input is either ...

Highly Accurate Dichotomous Image Segmentation (Xuebin Qin, Hang Dai, Xiaobin Hu, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2022)

We present a systematic study on a new task called dichotomous image segmentation (DIS), which aims to segment highly accurate objects from natural images. To this end, we collected the first large-scale DIS dataset, called ...

Semi-Supervised Cross-Modal Salient Object Detection with U-Structure Networks (Yunqing Bao, Hang Dai and Abdulmotaleb Elsaddik) arXiv (2022)

Salient Object Detection (SOD) is a popular and important topic aimed at precise detection and segmentation of the interesting regions in the images. We integrate the linguistic information into the vision-based U-Structure networks designed for ...

Balanced Self-Paced Learning for AUC Maximization (Hang Dai) arXiv (2022)

Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. ...

High-resolution Iterative Feedback Network for Camouflaged Object Detection (Xiaobin Hu, Deng-Ping Fan, Xuebin Qin, et al.) arXiv (2022)

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 ...

Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving (Yi-Nan Chen, Hang Dai and Yong Ding) arXiv (2022)

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-based 3D detection architectures. The advanced stereo 3D detectors can also ...

Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images (Zhou Huang, Xiang Tianzhu, Huaixin Chen, et al.) arXiv (2022)

Existing CNNs-based salient object detection (SOD) heavily depends on the large-scale pixel-level annotations, which is labor-intensive, time-consuming, and expensive. By contrast, the sparse annotations (e.g., image-level or scribble) become appealing to the salient object detection ...

CpT: Convolutional point transformer for 3D point cloud processing (Chaitanya Kaul, Joshua Mitton, Hang Dai, et al.) arXiv (2021)

We present CpT: Convolutional point Transformer - a novel deep learning architecture for dealing with the unstructured nature of 3D point cloud data. CpT is an improvement over existing attention-based Convolutions Neural Networks as well ...

Adversarially robust deepfake media detection using fused convolutional neural network predictions (Sohail Ahmed Khan, Alessandro Artusi and Hang Dai) arXiv (2021)

Deepfakes are synthetically generated images, videos or audios, which fraudsters use to manipulate legitimate information. Current deepfake detection systems struggle against unseen data. To address this, we employ three different deep Convolutional Neural Network (CNN) ...

3D IoU-Net: IoU Guided 3D object detector for point clouds (Jiale Li, Shujie Luo, Ziqi Zhu, et al.) arXiv (2020)

LiDAR point clouds is essential but challenging for 3D object detection. Most existing methods focus on classification and bounding box regression in the way of designing effective network to learn expressive feature from this irregular ...