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Amogh Joshi

4 papers hereh-index 7310 citations8 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
same name
  • Amogh Joshi — 6 papers, h 3
  • Amogh Joshi — 2 papers, h 2
  • Amogh Joshi — 2 papers, h 2
  • Amogh Joshi — 1 paper
  • Amogh Joshi — 1 paper
  • Amogh Joshi — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedNTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

3 citations · 3 across the 2 of their papers we have counts for

collaborators

4 papers

cs.CV2026

WorldFlow3D: Flowing Through 3D Distributions for Unbounded World Generation

Amogh Joshi, Julian Ost, Felix Heide

Unbounded 3D world generation is emerging as a foundational task for scene modeling in computer vision, graphics, and robotics. In this work, we present WorldFlow3D, a novel method…

cs.CV2024★ 3 cited

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Xiaoning Liu, Zongwei Wu, Ao Li +109

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective netw…

cs.CV2024

The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report

Bin Ren, Yawei Li, Nancy Mehta +129

This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this cha…

cs.CV2024

NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results

Zheng Chen, Zongwei Wu, Eduard Zamfir +85

This paper reviews the NTIRE 2024 challenge on image super-resolution (×4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.