ICCV 2023视觉持续学习挑战赛技术报告：用于语义分割的连续测试时自适应
Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation
AI总结：
本文提出一种用于语义分割的连续测试时自适应方法，通过分析分布偏移并适应数据动态，在ICCV 2023挑战赛中获得第三名及创新奖。
摘要：
该挑战赛的目标是开发一种测试时自适应（TTA）方法，使其能够针对语义分割任务，适应视频序列中逐渐变化的域。该挑战基于一个合成驾驶视频数据集——SHIFT。源模型是在晴天白天拍摄的图像上训练的。测试时的域变化主要由不断变化的天气条件和一天中的时间引起。TTA方法在每个图像序列（视频）中单独评估，这意味着在下一个序列之前，模型会被重置为源模型状态。图像逐帧到达，并且必须在每一帧到达时做出预测。每个序列由401张图像组成，从源域开始，然后逐渐漂移到不同的域（变化的天气或一天中的时间），直到序列的中间部分。在序列的后半部分，域逐渐转变回源域。真实标签数据仅适用于SHIFT数据集的验证集，其中只有六个以源域开始和结束的序列。我们专门针对这些序列进行了分析。用于排行榜排名评估的测试集真实标签数据未公开。\n 我们提出的解决方案在挑战赛中获得了第三名，并获得了创新奖。与得分更高的解决方案相反，我们没有使用任何外部预训练模型或专门的数据增强，以保持解决方案尽可能通用。我们专注于分析分布偏移，并开发一种能够适应不断变化的数据动态并跨不同场景泛化的方法。
英文摘要：
The goal of the challenge is to develop a test-time adaptation (TTA) method, which could adapt the model to gradually changing domains in video sequences for semantic segmentation task. It is based on a synthetic driving video dataset - SHIFT. The source model is trained on images taken during daytime in clear weather. Domain changes at test-time are mainly caused by varying weather conditions and times of day. The TTA methods are evaluated in each image sequence (video) separately, meaning the model is reset to the source model state before the next sequence. Images come one by one and a prediction has to be made at the arrival of each frame. Each sequence is composed of 401 images and starts with the source domain, then gradually drifts to a different one (changing weather or time of day) until the middle of the sequence. In the second half of the sequence, the domain gradually shifts back to the source one. Ground truth data is available only for the validation split of the SHIFT dataset, in which there are only six sequences that start and end with the source domain. We conduct an analysis specifically on those sequences. Ground truth data for test split, on which the developed TTA methods are evaluated for leader board ranking, are not publicly available. The proposed solution secured a 3rd place in a challenge and received an innovation award. Contrary to the solutions that scored better, we did not use any external pretrained models or specialized data augmentations, to keep the solutions as general as possible. We have focused on analyzing the distributional shift and developing a method that could adapt to changing data dynamics and generalize across different scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
