Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations-Reference-Cited by-同舟云学术

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Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations

Published:2022-01 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Beal Josh¹,Wu Hao-Yu¹,Park Dong Huk¹,Zhai Andrew¹,Kislyuk Dmitry¹

Affiliation:

1. Pinterest

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9706406/9706408/09706652.pdf?arnumber=9706652

Reference44 articles.

1. A large-scale study of representation learning with the visual task adaptation benchmark;zhai,2019

2. Learning a Unified Embedding for Visual Search at Pinterest

3. Aggregated Residual Transformations for Deep Neural Networks

4. Sampling Matters in Deep Embedding Learning

5. Attention is all you need;vaswani;NeurIPS,2017

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1. Bringing Multimodality to Amazon Visual Search System;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search;Companion Proceedings of the ACM Web Conference 2024;2024-05-13

3. AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

4. Analyzing I/O Performance of a Hierarchical HPC Storage System for Distributed Deep Learning;Parallel and Distributed Computing, Applications and Technologies;2023

5. MultiBiSage;Proceedings of the VLDB Endowment;2022-12

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