Back to Search Start Over

Champion Solution for the WSDM2023 Toloka VQA Challenge

Authors :
Gao, Shengyi
Chen, Zhe
Chen, Guo
Wang, Wenhai
Lu, Tong
Publication Year :
2023

Abstract

In this report, we present our champion solution to the WSDM2023 Toloka Visual Question Answering (VQA) Challenge. Different from the common VQA and visual grounding (VG) tasks, this challenge involves a more complex scenario, i.e. inferring and locating the object implicitly specified by the given interrogative question. For this task, we leverage ViT-Adapter, a pre-training-free adapter network, to adapt multi-modal pre-trained Uni-Perceiver for better cross-modal localization. Our method ranks first on the leaderboard, achieving 77.5 and 76.347 IoU on public and private test sets, respectively. It shows that ViT-Adapter is also an effective paradigm for adapting the unified perception model to vision-language downstream tasks. Code and models will be released at https://github.com/czczup/ViT-Adapter/tree/main/wsdm2023.<br />Comment: Technical report in WSDM Cup 2023

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2301.09045
Document Type :
Working Paper