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20212023
most citedUnsupervised Adaptation of Semantic Segmentation Models without Source Data

4 citations · 7 across the 5 of their papers we have counts for

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cs.CV2023

Analyzing the Efficacy of an LLM-Only Approach for Image-based Document Question Answering

Nidhi Hegde, Sujoy Paul, Gagan Madan +1

Recent document question answering models consist of two key components: the vision encoder, which captures layout and visual elements in images, and a Large Language Model (LLM) t…

cs.CV2023

Is it an i or an l: Test-time Adaptation of Text Line Recognition Models

Debapriya Tula, Sujoy Paul, Gagan Madan +3

Recognizing text lines from images is a challenging problem, especially for handwritten documents due to large variations in writing styles. While text line recognition models are…

cs.CV20232 cited

Weakly supervised information extraction from inscrutable handwritten document images

Sujoy Paul, Gagan Madan, Akankshya Mishra +3

State-of-the-art information extraction methods are limited by OCR errors. They work well for printed text in form-like documents, but unstructured, handwritten documents still rem…

cs.CV20221 cited

Novel Class Discovery without Forgetting

K J Joseph, Sujoy Paul, Gaurav Aggarwal +4

Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. I…

cs.CV20214 cited

Unsupervised Adaptation of Semantic Segmentation Models without Source Data

Sujoy Paul, Ansh Khurana, Gaurav Aggarwal

We consider the novel problem of unsupervised domain adaptation of source models, without access to the source data for semantic segmentation. Unsupervised domain adaptation aims t…