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Rakesh Ranjan

4 papers here

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

author position
  • middle author3
  • last author1

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

fields
  • cs.CV4
ORCID 0000-0003-0963-4512
same name
  • Rakesh Ranjan — 4 papers, h 9
  • Rakesh Ranjan — 2 papers
  • Rakesh Ranjan — 2 papers
  • Rakesh Ranjan — 1 paper
  • Rakesh Ranjan — 1 paper

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 citedEfficient and Explicit Modelling of Image Hierarchies for Image Restoration

18 citations · 21 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2023★ 1 cited

MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition

Xinyu Gong, Sreyas Mohan, Naina Dhingra +4

In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data…

cs.CV2023

Learning Neural Duplex Radiance Fields for Real-Time View Synthesis

Ziyu Wan, Christian Richardt, Aljaž Božič +8

Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer pe…

cs.CV2023★ 2 cited

AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation

Hyunyoung Jung, Zhuo Hui, Lei Luo +5

To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs. However, downsizing inputs makes the esti…

cs.CV2023★ 18 cited

Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

Yawei Li, Yuchen Fan, Xiaoyu Xiang +4

The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve tha…

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