Search Results for author: Hang Dai

Found 15 papers, 3 papers with code

CpT: Convolutional Point Transformer for 3D Point Cloud Processing

no code implementations21 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.

Semantic Segmentation

Video Transformer for Deepfake Detection with Incremental Learning

no code implementations11 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.

3D Face Reconstruction DeepFake Detection +2

Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud

2 code implementations8 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.

3D Object Detection

From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder

1 code implementation8 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.

3D Object Detection Region Proposal

M3DSSD: Monocular 3D Single Stage Object Detector

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.

Depth Estimation Monocular 3D Object Detection

Adversarially robust deepfake media detection using fused convolutional neural network predictions

no code implementations11 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.

Adversarial Attack DeepFake Detection +1

A Human Ear Reconstruction Autoencoder

no code implementations7 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.

3D Face Reconstruction Self-Supervised Learning

3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds

no code implementations10 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.

FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation

no code implementations4 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.

Medical Image Segmentation

Penalizing small errors using an Adaptive Logarithmic Loss

no code implementations22 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.

Retinal Vessel Segmentation

PointAE: Point Auto-Encoder for 3D Statistical Shape and Texture Modelling

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.

Non-rigid 3D Shape Registration using an Adaptive Template

no code implementations21 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.

A 3D Morphable Model of Craniofacial Shape and Texture Variation

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.

Optical Flow Estimation

Functional Faces: Groupwise Dense Correspondence Using Functional Maps

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.