区分干扰物与背景，提升视频目标分割精度
Distractor-Aware Video Object Segmentation
- 采用一对多策略，将干扰物单独分类处理
- 在DAVIS 2017测试集上提升4.6个百分点
- 适合需要高精度分割的复杂视频场景
半监督视频对象分割任务旨在给定首帧初始掩码的情况下，对整个视频序列中的目标进行分割。现有判别方法通常将问题建模为目标与背景的一对一分类，但在实际视频中常存在与目标视觉相似的干扰物，易导致误检。为此，本文提出将干扰物独立划分为一类的“一对多”框架，使模型更关注易出错区域。通过改造LWL方法实现干扰物感知，该方法在DAVIS 2017验证集上达到新最优性能，在测试开发集上相比基线提升4.6个百分点。
原文摘要 · Abstract (English)
Semi-supervised video object segmentation is a challenging task that aims to segment a target throughout a video sequence given an initial mask at the first frame. Discriminative approaches have demonstrated competitive performance on this task at a sensible complexity. These approaches typically formulate the problem as a one-versus-one classification between the target and the background. However, in reality, a video sequence usually encompasses a target, background, and possibly other distracting objects. Those objects increase the risk of introducing false positives, especially if they share visual similarities with the target. Therefore, it is more effective to separate distractors from the background, and handle them independently. We propose a one-versus-many scheme to address this situation by separating distractors into their own class. This separation allows imposing special attention to challenging regions that are most likely to degrade the performance. We demonstrate the prominence of this formulation by modifying the learning-what-to-learn (LWL) method to be distractor-aware. Our proposed approach sets a new state-of-the-art on the DAVIS 2017 val dataset, and improves over the baseline on the DAVIS 2017 test-dev benchmark by 4.6 percentage points.
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