4 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
Semantic Scene Completion with Multi-Feature Data Balancing Network
Mona Alawadh, Mahesan Niranjan, Hansung Kim
Semantic Scene Completion (SSC) is a critical task in computer vision, that utilized in applications such as virtual reality (VR). SSC aims to construct detailed 3D models from par…
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging
Keqiang Fan, Xiaohao Cai, Mahesan Niranjan
Unlike typical visual scene recognition domains, in which massive datasets are accessible to deep neural networks, medical image interpretations are often obstructed by the paucity…
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation
Yihong Wu, Yuwen Heng, Mahesan Niranjan +1
Depth estimation from a single image is a challenging problem in computer vision because binocular disparity or motion information is absent. Whereas impressive performances have b…
IIHT: Medical Report Generation with Image-to-Indicator Hierarchical Transformer
Keqiang Fan, Xiaohao Cai, Mahesan Niranjan
Automated medical report generation has become increasingly important in medical analysis. It can produce computer-aided diagnosis descriptions and thus significantly alleviate the…
GO-LDA: Generalised Optimal Linear Discriminant Analysis
Jiahui Liu, Xiaohao Cai, Mahesan Niranjan
Linear discriminant analysis (LDA) has been a useful tool in pattern recognition and data analysis research and practice. While linearity of class boundaries cannot always be expec…
A Biologically Inspired Visual Working Memory for Deep Networks
Ethan Harris, Mahesan Niranjan, Jonathon Hare
The ability to look multiple times through a series of pose-adjusted glimpses is fundamental to human vision. This critical faculty allows us to understand highly complex visual sc…