no code implementations • 25 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.
1 code implementation • 27 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.
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.
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.
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.
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.
Ranked #5 on Semantic Contour Prediction on Sbd val
no code implementations • 3 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.