266 citations · 981 across the 59 of their papers we have counts for
14 papers · 1 filter
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…