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A. Goel

14 papers hereh-index 7306 citations24 works total

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

author position
  • first author7
  • middle author6
  • last author1

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CV11
  • cs.CL3
same name
  • A. Goel — 18 papers, h 13
  • A. Goel — 11 papers, h 4
  • A. Goel — 5 papers, h 14
  • A. Goel — 2 papers, h 13
  • A. Goel — 2 papers, h 2
  • A. Goel — 1 paper, h 10

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

activity
20192026
most citedAn End-to-End Network for Generating Social Relationship Graphs

7 citations · 17 across the 13 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CV2022★ 1 cited

Who are you referring to? Coreference resolution in image narrations

Arushi Goel, Basura Fernando, Frank Keller +1

Coreference resolution aims to identify words and phrases which refer to same entity in a text, a core task in natural language processing. In this paper, we extend this task to re…

cs.CV2022

WiCV 2022: The Tenth Women In Computer Vision Workshop

Doris Antensteiner, Silvia Bucci, Arushi Goel +6

In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2022, organized alongside the hybrid CVPR 2022 in New Orleans, Louisiana. It provides a voice to a…

cs.CV2022

WiCV 2021: The Eighth Women In Computer Vision Workshop

Arushi Goel, Niveditha Kalavakonda, Nour Karessli +5

In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2021, organized alongside the virtual CVPR 2021. It provides a voice to a minority (female) group…

cs.CV2022★ 5 cited

PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels

Arushi Goel, Yunlong Jiao, Jordan Massiah

Acquiring accurate labels on large-scale datasets is both time consuming and expensive. To reduce the dependency of deep learning models on learning from clean labeled data, severa…

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