Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Bober, Mirosław"'
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. In the current literature, such task is universally solved via a
Externí odkaz:
http://arxiv.org/abs/2206.11352
Autor:
Husain, Syed Sameed, Ong, Eng-Jon, Minskiy, Dmitry, Bober-Irizar, Mikel, Irizar, Amaia, Bober, Miroslaw
Unravelling protein distributions within individual cells is key to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly label
Externí odkaz:
http://arxiv.org/abs/2205.09841
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. Currently, the message passing neural network based mean field vari
Externí odkaz:
http://arxiv.org/abs/2205.07017
Autor:
Minskiy, Dmitry, Bober, Miroslaw
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. However, their performance has yet t
Externí odkaz:
http://arxiv.org/abs/2203.15392
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. Currently, the mean field variational Bayesian framework is the de facto meth
Externí odkaz:
http://arxiv.org/abs/2201.11697
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. Currently, such approximati
Externí odkaz:
http://arxiv.org/abs/2112.05727
Autor:
Perez-Ortiz, Maria, Rivasplata, Omar, Guedj, Benjamin, Gleeson, Matthew, Zhang, Jingyu, Shawe-Taylor, John, Bober, Miroslaw, Kittler, Josef
Recent works have investigated deep learning models trained by optimising PAC-Bayes bounds, with priors that are learnt on subsets of the data. This combination has been shown to lead not only to accurate classifiers, but also to remarkably tight ris
Externí odkaz:
http://arxiv.org/abs/2109.10304
The process of aggregation is ubiquitous in almost all deep nets models. It functions as an important mechanism for consolidating deep features into a more compact representation, whilst increasing robustness to overfitting and providing spatial inva
Externí odkaz:
http://arxiv.org/abs/2107.04458
We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio (SNR) of deep convolutional feature maps
Externí odkaz:
http://arxiv.org/abs/1907.05794
Autor:
Husain, Syed Sameed, Bober, Miroslaw
Publikováno v:
IEEE Transactions on Image Processing, Early Access 22 May 2019
This paper addresses the problem of very large-scale image retrieval, focusing on improving its accuracy and robustness. We target enhanced robustness of search to factors such as variations in illumination, object appearance and scale, partial occlu
Externí odkaz:
http://arxiv.org/abs/1906.06626