266 citations · 981 across the 59 of their papers we have counts for
67 papers
Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak Labels
Nikhel Gupta, Zeeshan Hayder, Ray P. Norris +7
The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple comp…
Hyperbolic Audio-visual Zero-shot Learning
Jie Hong, Zeeshan Hayder, Junlin Han +3
Audio-visual zero-shot learning aims to classify samples consisting of a pair of corresponding audio and video sequences from classes that are not present during training. An analy…
Automatic Illumination Spectrum Recovery
Nariman Habili, Jeremy Oorloff, Lars Petersson
We develop a deep learning network to estimate the illumination spectrum of hyperspectral images under various lighting conditions. To this end, a dataset, IllumNet, was created. I…
CVB: A Video Dataset of Cattle Visual Behaviors
Ali Zia, Renuka Sharma, Reza Arablouei +7
Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the u…
Topological Deep Learning: A Review of an Emerging Paradigm
Ali Zia, Abdelwahed Khamis, James Nichols +3
Topological data analysis (TDA) provides insight into data shape. The summaries obtained by these methods are principled global descriptions of multi-dimensional data whilst exhibi…
A Hyperspectral and RGB Dataset for Building Facade Segmentation
Nariman Habili, Ernest Kwan, Weihao Li +4
Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications. This work introduces an HSI dataset of building facades in…
PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning
Jie Hong, Shi Qiu, Weihao Li +4
Point cloud learning is receiving increasing attention. However, most existing point cloud models lack the practical ability to deal with the unavoidable presence of unknown object…
What Images are More Memorable to Machines?
Junlin Han, Huangying Zhan, Jie Hong +4
This paper studies the problem of measuring and predicting how memorable an image is to pattern recognition machines, as a path to explore machine intelligence. Firstly, we propose…
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning
Changkun Ye, Nick Barnes, Lars Petersson +1
Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process. However, the problem of class imbalance is rarely discussed, despite i…
Learning Deep Optimal Embeddings with Sinkhorn Divergences
Soumava Kumar Roy, Yan Han, Mehrtash Harandi +1
Deep Metric Learning algorithms aim to learn an efficient embedding space to preserve the similarity relationships among the input data. Whilst these algorithms have achieved signi…
Curved Geometric Networks for Visual Anomaly Recognition
Jie Hong, Pengfei Fang, Weihao Li +3
Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature. However, the success of the geomet…
CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping
Junlin Han, Lars Petersson, Hongdong Li +1
We present a simple method, CropMix, for the purpose of producing a rich input distribution from the original dataset distribution. Unlike single random cropping, which may inadver…
Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging
David Ahmedt-Aristizabal, Chuong Nguyen, Lachlan Tychsen-Smith +5
Advanced artificial intelligence and machine learning have great potential to redefine how skin lesions are detected, mapped, tracked and documented. Here, We propose a 3D whole-bo…
Pyramidal Attention for Saliency Detection
Tanveer Hussain, Abbas Anwar, Saeed Anwar +2
Salient object detection (SOD) extracts meaningful contents from an input image. RGB-based SOD methods lack the complementary depth clues; hence, providing limited performance for…
Towards Open-Set Object Detection and Discovery
Jiyang Zheng, Weihao Li, Jie Hong +2
With the human pursuit of knowledge, open-set object detection (OSOD) has been designed to identify unknown objects in a dynamic world. However, an issue with the current setting i…
Learning Dense Correspondence from Synthetic Environments
Mithun Lal, Anthony Paproki, Nariman Habili +3
Estimation of human shape and pose from a single image is a challenging task. It is an even more difficult problem to map the identified human shape onto a 3D human model. Existing…
GOSS: Towards Generalized Open-set Semantic Segmentation
Jie Hong, Weihao Li, Junlin Han +4
In this paper, we present and study a new image segmentation task, called Generalized Open-set Semantic Segmentation (GOSS). Previously, with the well-known open-set semantic segme…
Continuous Human Action Recognition for Human-Machine Interaction: A Review
Harshala Gammulle, David Ahmedt-Aristizabal, Simon Denman +3
With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams…
You Only Cut Once: Boosting Data Augmentation with a Single Cut
Junlin Han, Pengfei Fang, Weihao Li +5
We present You Only Cut Once (YOCO) for performing data augmentations. YOCO cuts one image into two pieces and performs data augmentations individually within each piece. Applying…
Transcribing Natural Languages for The Deaf via Neural Editing Programs
Dongxu Li, Chenchen Xu, Liu Liu +4
This work studies the task of glossification, of which the aim is to em transcribe natural spoken language sentences for the Deaf (hard-of-hearing) community to ordered sign langua…
In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains
Ting Cao, Mohammad Ali Armin, Simon Denman +2
Medical applications have benefited greatly from the rapid advancement in computer vision. Considering patient monitoring in particular, in-bed human posture estimation offers impo…
Towards a Robust Differentiable Architecture Search under Label Noise
Christian Simon, Piotr Koniusz, Lars Petersson +2
Neural Architecture Search (NAS) is the game changer in designing robust neural architectures. Architectures designed by NAS outperform or compete with the best manual network desi…
Feature Correlation Aggregation: on the Path to Better Graph Neural Networks
Jieming Zhou, Tong Zhang, Pengfei Fang +2
Prior to the introduction of Graph Neural Networks (GNNs), modeling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The co…
Blind Image Decomposition
Junlin Han, Weihao Li, Pengfei Fang +5
We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that…