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.

本文介绍了我们参与 CVPR2022 AVA 挑战赛的技术细节，包括数据增强和模型选择、训练策略、和整合不同分割框架等方面，实验结果表明我们的方法在 AVA 测试集上取得了显著的效果。

CVPR2022 AVA 辅助视觉与自主性挑战赛第三名解决方案

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

Intelligent agents should have the ability to leverage knowledge from
previously learned tasks in order to learn new ones quickly and efficiently.
Meta-learning approaches have emerged as a popular solution to achieve this.
However, meta-reinforcement learning (meta-RL) algorithms have thus far been
restricted to simple environments with narrow task distributions. Moreover, the
paradigm of pretraining followed by fine-tuning to adapt to new tasks has
emerged as a simple yet effective solution in supervised and self-supervised
learning. This calls into question the benefits of meta-learning approaches
also in reinforcement learning, which typically come at the cost of high
complexity. We hence investigate meta-RL approaches in a variety of
vision-based benchmarks, including Procgen, RLBench, and Atari, where
evaluations are made on completely novel tasks. Our findings show that when
meta-learning approaches are evaluated on different tasks (rather than
different variations of the same task), multi-task pretraining with fine-tuning
on new tasks performs equally as well, or better, than meta-pretraining with
meta test-time adaptation. This is encouraging for future research, as
multi-task pretraining tends to be simpler and computationally cheaper than
meta-RL. From these findings, we advocate for evaluating future meta-RL methods
on more challenging tasks and including multi-task pretraining with fine-tuning
as a simple, yet strong baseline.

本文研究使用 meta - 强化学习方法来完成多个视觉基准测试，在评估这些方法时，我们发现在不同的任务上，多任务预训练加微调的性能相当甚至更好，这与使用 meta-pretraining 和 meta test-time adaptation 的表现相当。因此我们建议在未来的 meta-RL 中加入更具挑战性的任务以及多任务预训练和微调作为更简单，强大的基线。