2 citations · 3 across the 5 of their papers we have counts for
7 papers · 1 filter
Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention
Wenbo Wei, Jun Wang, Shan Raza +1
Panoptic segmentation in complex scenes remains challenging because of occlusions, yet modern approaches often neglect occlusion modelling. In this paper, we propose Position Embed…
Improving Medical Visual Representation Learning with Pathological-level Cross-Modal Alignment and Correlation Exploration
Jun Wang, Lixing Zhu, Xiaohan Yu +2
Learning medical visual representations from image-report pairs through joint learning has garnered increasing research attention due to its potential to alleviate the data scarcit…
COCO-OLAC: A Benchmark for Occluded Panoptic Segmentation and Image Understanding
Wenbo Wei, Jun Wang, Abhir Bhalerao
To help address the occlusion problem in panoptic segmentation and image understanding, this paper proposes a new large-scale dataset named COCO-OLAC (COCO Occlusion Labels for All…
Scene Graph Aided Radiology Report Generation
Jun Wang, Lixing Zhu, Abhir Bhalerao +1
Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports. The scene graph contains rich information to describe the…
Can Prompt Learning Benefit Radiology Report Generation?
Jun Wang, Lixing Zhu, Abhir Bhalerao +1
Radiology report generation aims to automatically provide clinically meaningful descriptions of radiology images such as MRI and X-ray. Although great success has been achieved in…
A QuadTree Image Representation for Computational Pathology
Rob Jewsbury, Abhir Bhalerao, Nasir Rajpoot
The field of computational pathology presents many challenges for computer vision algorithms due to the sheer size of pathology images. Histopathology images are large and need to…