Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Sachdeva, Ragav"'
Enabling engagement of manga by visually impaired individuals presents a significant challenge due to its inherently visual nature. With the goal of fostering accessibility, this paper aims to generate a dialogue transcript of a complete manga chapte
Externí odkaz:
http://arxiv.org/abs/2408.00298
Autor:
Sachdeva, Ragav, Zisserman, Andrew
In the past few decades, Japanese comics, commonly referred to as Manga, have transcended both cultural and linguistic boundaries to become a true worldwide sensation. Yet, the inherent reliance on visual cues and illustration within manga renders it
Externí odkaz:
http://arxiv.org/abs/2401.10224
Autor:
Sachdeva, Ragav, Zisserman, Andrew
The goal of this paper is to detect what has changed, if anything, between two "in the wild" images of the same 3D scene acquired from different camera positions and at different temporal instances. The open-set nature of this problem, occlusions/dis
Externí odkaz:
http://arxiv.org/abs/2308.10417
Autor:
Sachdeva, Ragav, Zisserman, Andrew
We live in a dynamic world where things change all the time. Given two images of the same scene, being able to automatically detect the changes in them has practical applications in a variety of domains. In this paper, we tackle the change detection
Externí odkaz:
http://arxiv.org/abs/2209.14341
Autor:
Sachdeva, Ragav, Hammond, Ravi, Bockman, James, Arthur, Alec, Smart, Brandon, Craggs, Dustin, Doan, Anh-Dzung, Rowntree, Thomas, Schutz, Elijah, Orenstein, Adrian, Yu, Andy, Chin, Tat-Jun, Reid, Ian
Future Moon bases will likely be constructed using resources mined from the surface of the Moon. The difficulty of maintaining a human workforce on the Moon and communications lag with Earth means that mining will need to be conducted using collabora
Externí odkaz:
http://arxiv.org/abs/2109.12109
We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe label noise and competitive robustness to non-severe label noise problems, in comparison to the
Externí odkaz:
http://arxiv.org/abs/2103.11395
Deep neural network models are robust to a limited amount of label noise, but their ability to memorise noisy labels in high noise rate problems is still an open issue. The most competitive noisy-label learning algorithms rely on a 2-stage process co
Externí odkaz:
http://arxiv.org/abs/2103.04173
The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such large-scale data sets with precise annotations is very expensive and time
Externí odkaz:
http://arxiv.org/abs/2011.05704
Many real-world optimisation problems involve dynamic and stochastic components. While problems with multiple interacting components are omnipresent in inherently dynamic domains like supply-chain optimisation and logistics, most research on dynamic
Externí odkaz:
http://arxiv.org/abs/2004.12045
Autor:
Sachdeva, Ragav, Cordeiro, Filipe Rolim, Belagiannis, Vasileios, Reid, Ian, Carneiro, Gustavo
Publikováno v:
In Pattern Recognition February 2023 134
