no code implementations • 13 Jul 2017 • Mikel Bober-Irizar, Sameed Husain, Eng-Jon Ong, Miroslaw Bober
We investigate factors controlling DNN diversity in the context of the Google Cloud and YouTube-8M Video Understanding Challenge.
no code implementations • 1 Feb 2017 • Eng-Jon Ong, Sameed Husain, Miroslaw Bober
To achieve this, we propose a novel Siamese network.
no code implementations • 22 Jul 2016 • Santosh Tirunagari, Norman Poh, Miroslaw Bober, David Windridge
A background model describes a scene without any foreground objects and has a number of applications, ranging from video surveillance to computational photography.
no code implementations • 3 Jul 2018 • Eng-Jon Ong, Sameed Husain, Mikel Bober-Irizar, Miroslaw Bober
This work addresses the problem of accurate semantic labelling of short videos.
no code implementations • CVPR 2016 • Eng-Jon Ong, Miroslaw Bober
To this end, we propose a novel, unsupervised approach to thresholded search in Hamming space, supporting long codes (e. g. 512-bits) with a wide-range of Hamming distance radii.
no code implementations • 13 Mar 2019 • Daqi Liu, Miroslaw Bober, Josef Kittler
Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual semantic information pursuit methods.
no code implementations • 24 May 2019 • Santosh Tirunagari, Norman Poh, Kevin Wells, Miroslaw Bober, Isky Gorden, David Windridge
Traditionally, human experts are required to manually delineate the kidney ROI across multiple images in the dynamic sequence.
no code implementations • 26 May 2019 • Santosh Tirunagari, Norman Poh, Kevin Wells, Miroslaw Bober, Isky Gorden, David Windridge
To address this issue, we present Dynamic Mode Decomposition (DMD) coupled with thresholding and blob analysis as a framework for automatic delineation of the kidney region.
no code implementations • 15 Jun 2019 • Syed Sameed Husain, Miroslaw Bober
On image retrieval datasets Holidays, Oxford and MPEG, the REMAP descriptor achieves mAP of 95. 5%, 91. 5%, and 80. 1% respectively, outperforming any results published to date.
no code implementations • 12 Jul 2019 • Syed Sameed Husain, Eng-Jon Ong, Miroslaw Bober
We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets.
no code implementations • WS 2019 • Austin Kershaw, Miroslaw Bober
The challenge of automatically describing images and videos has stimulated much research in Computer Vision and Natural Language Processing.
no code implementations • 9 Jul 2021 • Eng-Jon Ong, Sameed Husain, Miroslaw Bober
However, this requires the knowledge of the distributions of the activations of aggregation layers.
no code implementations • 21 Sep 2021 • Maria Perez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, Josef Kittler
We experiment on 6 datasets with different strategies and amounts of data to learn data-dependent PAC-Bayes priors, and we compare them in terms of their effect on test performance of the learnt predictors and tightness of their risk certificate.
no code implementations • 10 Dec 2021 • Daqi Liu, Miroslaw Bober, Josef Kittler
Scene graph generation aims to interpret an input image by explicitly modelling the potential objects and their relationships, which is predominantly solved by the message passing neural network models in previous methods.
no code implementations • 27 Jan 2022 • Daqi Liu, Miroslaw Bober, Josef Kittler
As a structured prediction task, scene graph generation aims to build a visually-grounded scene graph to explicitly model objects and their relationships in an input image.
no code implementations • 29 Mar 2022 • Dmitry Minskiy, Miroslaw Bober
Recent work showed that hybrid networks, which combine predefined and learnt filters within a single architecture, are more amenable to theoretical analysis and less prone to overfitting in data-limited scenarios.
no code implementations • 14 May 2022 • Daqi Liu, Miroslaw Bober, Josef Kittler
Scene graph generation is a structured prediction task aiming to explicitly model objects and their relationships via constructing a visually-grounded scene graph for an input image.
no code implementations • 19 May 2022 • Syed Sameed Husain, Eng-Jon Ong, Dmitry Minskiy, Mikel Bober-Irizar, Amaia Irizar, Miroslaw Bober
Unravelling protein distributions within individual cells is key to understanding their function and state and indispensable to developing new treatments.
no code implementations • 22 Jun 2022 • Daqi Liu, Miroslaw Bober, Josef Kittler
As a structured prediction task, scene graph generation, given an input image, aims to explicitly model objects and their relationships by constructing a visually-grounded scene graph.