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20202025
most citedLongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment

109 citations · 111 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.CV2025

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…

cs.CV2024

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…

cs.CV2024★ 2 cited

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…

cs.CV2023

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…

cs.CV2022

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…

cs.CV2021

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…

cs.CV2021★ 109 cited

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…