115 citations · 153 across the 7 of their papers we have counts for
11 papers
Dynamic Graph Message Passing Networks for Visual Recognition
Li Zhang, Mohan Chen, Anurag Arnab +2
Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although convolution neural networks (CNNs) have excelled in many vision tasks, they…
Dual Graph Convolutional Network for Semantic Segmentation
Li Zhang, Xiangtai Li, Anurag Arnab +3
Exploiting long-range contextual information is key for pixel-wise prediction tasks such as semantic segmentation. In contrast to previous work that uses multi-scale feature fusion…
Dynamic Graph Message Passing Networks
Li Zhang, Dan Xu, Anurag Arnab +1
Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although CNNs have excelled in many vision tasks, they are still limited in capturin…
Weakly- and Semi-Supervised Panoptic Segmentation
Qizhu Li, Anurag Arnab, Philip H. S. Torr
We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel…
On the Robustness of Semantic Segmentation Models to Adversarial Attacks
Anurag Arnab, Ondrej Miksik, Philip H. S. Torr
Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation. However, they have also been shown to…
Holistic, Instance-Level Human Parsing
Qizhu Li, Anurag Arnab, Philip H. S. Torr
Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there…
Pixelwise Instance Segmentation with a Dynamically Instantiated Network
Anurag Arnab, Philip H. S Torr
Semantic segmentation and object detection research have recently achieved rapid progress. However, the former task has no notion of different instances of the same object, and the…
A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials
Måns Larsson, Anurag Arnab, Fredrik Kahl +2
Are we using the right potential functions in the Conditional Random Field models that are popular in the Vision community? Semantic segmentation and other pixel-level labelling ta…
Bottom-up Instance Segmentation using Deep Higher-Order CRFs
Anurag Arnab, Philip H. S. Torr
Traditional Scene Understanding problems such as Object Detection and Semantic Segmentation have made breakthroughs in recent years due to the adoption of deep learning. However, t…
Joint Object-Material Category Segmentation from Audio-Visual Cues
Anurag Arnab, Michael Sapienza, Stuart Golodetz +4
It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very d…
Higher Order Conditional Random Fields in Deep Neural Networks
Anurag Arnab, Sadeep Jayasumana, Shuai Zheng +1
We address the problem of semantic segmentation using deep learning. Most segmentation systems include a Conditional Random Field (CRF) to produce a structured output that is consi…