PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data-Reference-Cited by-同舟云学术

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PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data

Published:2024-01-03 Issue: Volume:30 Page:6789-6801
ISSN:
Container-title:2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Herzig Roei¹,Abramovich Ofir²,Ben Avraham Elad¹,Arbelle Assaf³,Karlinsky Leonid⁴,Shamir Ariel²,Darrell Trevor⁵,Globerson Amir¹

Affiliation:

1. Tel-Aviv University

2. Reichman University

3. IBM Research

4. MIT-IBM Watson AI Lab

5. UC Berkeley

Funder

European Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10483279/10483282/10483731.pdf?arnumber=10483731

Reference115 articles.

1. Joint 2d-3d-semantic data for indoor scene understanding;Armeni,2017

2. ViViT: A Video Vision Transformer

3. Unified Graph Structured Models for Video Understanding

4. Attentional mixtures of soft prompt tuning for parameter-efficient multi-task knowledge sharing;Asai,2022

5. Bringing image scene structure to video via frame-clip consistency of object tokens;Avraham

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