3.5k citations · 14.1k across the 436 of their papers we have counts for
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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…
Learning to Compare: Relation Network for Few-Shot Learning
Flood Sung, Yongxin Yang, Li Zhang +3
We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. O…
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…
Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed Videos
Suman Saha, Gurkirt Singh, Michael Sapienza +2
Current state-of-the-art human action recognition is focused on the classification of temporally trimmed videos in which only one action occurs per frame. In this work we address t…
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
Arslan Chaudhry, Puneet K. Dokania, Philip H. S. Torr
We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task. We show that properly combining saliency and attention maps allows us…
End-to-end representation learning for Correlation Filter based tracking
Jack Valmadre, Luca Bertinetto, João F. Henriques +2
The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formula…
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Namhoon Lee, Wongun Choi, Paul Vernaza +3
We introduce a Deep Stochastic IOC RNN Encoderdecoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively pre…
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…
Multi-Agent Diverse Generative Adversarial Networks
Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri +2
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First…
Incremental Tube Construction for Human Action Detection
Harkirat Singh Behl, Michael Sapienza, Gurkirt Singh +3
Current state-of-the-art action detection systems are tailored for offline batch-processing applications. However, for online applications like human-robot interaction, current sys…
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar +4
Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing mul…
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…