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Yoav Artzi

Cornell University

64 papers hereh-index 3716.4k citations90 works total

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

author position
  • middle author16
  • last author43

Across the 59 of 64 papers where every author was matched, so the position is known.

fields
  • cs.CL44
  • cs.LG6
  • cs.RO5
  • cs.AI4
  • cs.CV4
  • cs.IR1
affiliations
  • Cornell University
Homepage
same name
  • Yoav Artzi — 2 papers, h 3
  • Yoav Artzi — 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

activity
20132026
most citedBERTScore: Evaluating Text Generation with BERT

2.4k citations · 3k across the 56 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2021

Who's Waldo? Linking People Across Text and Images

Claire Yuqing Cui, Apoorv Khandelwal, Yoav Artzi +2

We present a task and benchmark dataset for person-centric visual grounding, the problem of linking between people named in a caption and people pictured in an image. In contrast t…

cs.CV2020★ 20 cited

Retouchdown: Adding Touchdown to StreetLearn as a Shareable Resource for Language Grounding Tasks in Street View

Harsh Mehta, Yoav Artzi, Jason Baldridge +2

The Touchdown dataset (Chen et al., 2019) provides instructions by human annotators for navigation through New York City streets and for resolving spatial descriptions at a given l…

cs.CV2018★ 9 cited

Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments

Howard Chen, Alane Suhr, Dipendra Misra +2

We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task. We introduce the Touchdown task and dataset, where an agent mus…

cs.CV2018

Early Fusion for Goal Directed Robotic Vision

Aaron Walsman, Yonatan Bisk, Saadia Gabriel +4

Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline. M…

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