109 citations · 111 across the 8 of their papers we have counts for
10 papers
From Panels to Prose: Generating Literary Narratives from Comics
Ragav Sachdeva, Andrew Zisserman
Comics have long been a popular form of storytelling, offering visually engaging narratives that captivate audiences worldwide. However, the visual nature of comics presents a sign…
Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names
Ragav Sachdeva, Gyungin Shin, Andrew Zisserman
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 p…
The Manga Whisperer: Automatically Generating Transcriptions for Comics
Ragav Sachdeva, Andrew Zisserman
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 inh…
The Change You Want to See (Now in 3D)
Ragav Sachdeva, Andrew Zisserman
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 t…
The Change You Want to See
Ragav Sachdeva, Andrew Zisserman
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 i…
Autonomy and Perception for Space Mining
Ragav Sachdeva, Ravi Hammond, James Bockman +10
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…
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
Ragav Sachdeva, Filipe R Cordeiro, Vasileios Belagiannis +2
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 competitiv…
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Filipe R. Cordeiro, Ragav Sachdeva, Vasileios Belagiannis +2
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 co…
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Ragav Sachdeva, Filipe R. Cordeiro, Vasileios Belagiannis +2
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 lar…
The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms
Ragav Sachdeva, Frank Neumann, Markus Wagner
Many real-world optimisation problems involve dynamic and stochastic components. While problems with multiple interacting components are omnipresent in inherently dynamic domains l…