论文标题
特定于任务的数据增强和用于VIPRIORS实例细分挑战的推理处理
Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge
论文作者
论文摘要
实例细分广泛应用于图像编辑，图像分析和自动驾驶等。但是，数据不足是实际应用中的常见问题。视觉归纳先验（VIPRIORS）实例分割挑战集中在此问题上。数据有效的计算机视觉挑战的毒蛇要求竞争对手在数据缺陷的设置中从头开始训练模型，但是可以使用一些视觉归纳先验。为了解决Vipriors实例细分问题，我们设计了特定于任务的数据增强（TS-DA）策略和推理处理（TS-IP）策略。特定于任务数据增强策略的主要目的是解决数据缺陷问题。为了充分利用视觉归纳先验，我们设计了一种特定于任务的推理处理策略。我们证明了建议的方法在vipriors实例分割挑战上的适用性。应用的分割模型是基于SWIN碱基的CBNETV2主链的混合任务级联检测器。实验结果表明，提出的方法可以在2022 Vipriors实例分割挑战的测试集中获得竞争成果，而0.531 [email protected]：0.95。
Instance segmentation is applied widely in image editing, image analysis and autonomous driving, etc. However, insufficient data is a common problem in practical applications. The Visual Inductive Priors(VIPriors) Instance Segmentation Challenge has focused on this problem. VIPriors for Data-Efficient Computer Vision Challenges ask competitors to train models from scratch in a data-deficient setting, but there are some visual inductive priors that can be used. In order to address the VIPriors instance segmentation problem, we designed a Task-Specific Data Augmentation(TS-DA) strategy and Inference Processing(TS-IP) strategy. The main purpose of task-specific data augmentation strategy is to tackle the data-deficient problem. And in order to make the most of visual inductive priors, we designed a task-specific inference processing strategy. We demonstrate the applicability of proposed method on VIPriors Instance Segmentation Challenge. The segmentation model applied is Hybrid Task Cascade based detector on the Swin-Base based CBNetV2 backbone. Experimental results demonstrate that proposed method can achieve a competitive result on the test set of 2022 VIPriors Instance Segmentation Challenge, with 0.531 [email protected]:0.95.
