Search Results for author: Saumya Jetley

Found 7 papers, 5 papers with code

ShardNet: One Filter Set to Rule Them All

no code implementations25 Sep 2019 Saumya Jetley, Tommaso Cavallari, Philip Torr, Stuart Golodetz

Deep CNNs have achieved state-of-the-art performance for numerous machine learning and computer vision tasks in recent years, but as they have become increasingly deep, the number of parameters they use has also increased, making them hard to deploy in memory-constrained environments and difficult to interpret.

Learning Theory

Straight to Shapes++: Real-time Instance Segmentation Made More Accurate

1 code implementation27 May 2019 Laurynas Miksys, Saumya Jetley, Michael Sapienza, Stuart Golodetz, Philip H. S. Torr

The STS model can run at 35 FPS on a high-end desktop, but its accuracy is significantly worse than that of offline state-of-the-art methods.

Autonomous Driving Data Augmentation +5

With Friends Like These, Who Needs Adversaries?

1 code implementation NeurIPS 2018 Saumya Jetley, Nicholas A. Lord, Philip H. S. Torr

Via a novel experimental analysis, we illustrate some facts about deep convolutional networks for image classification that shed new light on their behaviour and how it connects to the problem of adversaries.

Adversarial Attack Classification +2

Learn To Pay Attention

4 code implementations ICLR 2018 Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip H. S. Torr

We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.

Adversarial Attack General Classification +3

End-to-End Saliency Mapping via Probability Distribution Prediction

1 code implementation CVPR 2016 Saumya Jetley, Naila Murray, Eleonora Vig

Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection.

Saliency Prediction Text Detection

Straight to Shapes: Real-time Detection of Encoded Shapes

1 code implementation CVPR 2017 Saumya Jetley, Michael Sapienza, Stuart Golodetz, Philip H. S. Torr

To achieve this, we use a denoising convolutional auto-encoder to establish an embedding space, and place the decoder after a fast end-to-end network trained to regress directly to the encoded shape vectors.

Decoder Denoising +2

Prototypical Priors: From Improving Classification to Zero-Shot Learning

no code implementations3 Dec 2015 Saumya Jetley, Bernardino Romera-Paredes, Sadeep Jayasumana, Philip Torr

Recent works on zero-shot learning make use of side information such as visual attributes or natural language semantics to define the relations between output visual classes and then use these relationships to draw inference on new unseen classes at test time.

Classification General Classification +1

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