Search Results for author: Ankush Gupta

Found 13 papers, 5 papers with code

Temporal Query Networks for Fine-grained Video Understanding

no code implementations CVPR 2021 Chuhan Zhang, Ankush Gupta, Andrew Zisserman

It attends to relevant segments for each query with a temporal attention mechanism, and can be trained using only the labels for each query.

Action Classification Video Understanding

Adaptive Text Recognition through Visual Matching

no code implementations ECCV 2020 Chuhan Zhang, Ankush Gupta, Andrew Zisserman

In this work, our objective is to address the problems of generalization and flexibility for text recognition in documents.

Representation Learning

CrossTransformers: spatially-aware few-shot transfer

2 code implementations NeurIPS 2020 Carl Doersch, Ankush Gupta, Andrew Zisserman

In this work, we illustrate how the neural network representations which underpin modern vision systems are subject to supervision collapse, whereby they lose any information that is not necessary for performing the training task, including information that may be necessary for transfer to new tasks or domains.

Self-Supervised Learning

Compliance Change Tracking in Business Process Services

no code implementations20 Aug 2019 Srikanth G Tamilselvam, Ankush Gupta, Arvind Agarwal

Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements.

Classification feature selection +1

Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos

no code implementations CVPR 2020 Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi

We propose KeypointGAN, a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses.

Facial Landmark Detection Image-to-Image Translation +4

Learning to Read by Spelling: Towards Unsupervised Text Recognition

no code implementations23 Sep 2018 Ankush Gupta, Andrea Vedaldi, Andrew Zisserman

This work presents a method for visual text recognition without using any paired supervisory data.

Inductive Visual Localisation: Factorised Training for Superior Generalisation

no code implementations21 Jul 2018 Ankush Gupta, Andrea Vedaldi, Andrew Zisserman

End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition.

Image Captioning Machine Translation +1

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