The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge

TL;DR

This paper presents a comprehensive approach for the CVPR2022 AVA Challenge, combining model optimization, data augmentation, and segmentation integration to achieve a competitive 63.008% AP score.

Contribution

The paper introduces a novel combination of training strategies and segmentation frameworks to enhance accessibility vision performance in the AVA challenge.

Findings

Achieved 63.008% AP@0.50:0.95 on AVA test set.

Effective data augmentation and training strategies improve performance.

Integration of segmentation frameworks further boosts results.

Abstract

The goal of AVA challenge is to provide vision-based benchmarks and methods relevant to accessibility. In this paper, we introduce the technical details of our submission to the CVPR2022 AVA Challenge. Firstly, we conducted some experiments to help employ proper model and data augmentation strategy for this task. Secondly, an effective training strategy was applied to improve the performance. Thirdly, we integrated the results from two different segmentation frameworks to improve the performance further. Experimental results demonstrate that our approach can achieve a competitive result on the AVA test set. Finally, our approach achieves 63.008\%AP@0.50:0.95 on the test set of CVPR2022 AVA Challenge.