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20222024
most citedThe Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes

6 citations · 17 across the 9 of their papers we have counts for

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5 papers · 1 filter

cs.CL20242 cited

Generating Contextually-Relevant Navigation Instructions for Blind and Low Vision People

Zain Merchant, Abrar Anwar, Emily Wang +2

Navigating unfamiliar environments presents significant challenges for blind and low-vision (BLV) individuals. In this work, we construct a dataset of images and goals across diffe…

cs.CL2024

WinoViz: Probing Visual Properties of Objects Under Different States

Woojeong Jin, Tejas Srinivasan, Jesse Thomason +1

Humans perceive and comprehend different visual properties of an object based on specific contexts. For instance, we know that a banana turns brown ``when it becomes rotten,'' wher…

cs.CL20236 cited

The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes

Lee Kezar, Elana Pontecorvo, Adele Daniels +6

Sign language recognition and translation technologies have the potential to increase access and inclusion of deaf signing communities, but research progress is bottlenecked by a l…

cs.CL2023

Exploring Strategies for Modeling Sign Language Phonology

Lee Kezar, Riley Carlin, Tejas Srinivasan +3

Like speech, signs are composed of discrete, recombinable features called phonemes. Prior work shows that models which can recognize phonemes are better at sign recognition, motiva…

cs.CL20233 cited

Improving Sign Recognition with Phonology

Lee Kezar, Jesse Thomason, Zed Sevcikova Sehyr

We use insights from research on American Sign Language (ASL) phonology to train models for isolated sign language recognition (ISLR), a step towards automatic sign language unders…