论文标题
对象检测的对比学习
Contrastive Learning for Object Detection
论文作者
论文摘要
对比学习通常用作一种自我监督学习的方法，“锚”和“正”是给定输入图像的两个随机增强，而“负”是所有其他图像的集合。但是，大批量和记忆库的要求使训练变得困难和缓慢。这促使有监督的对比方法的崛起通过使用带注释的数据来克服这些问题。我们希望通过基于其相似性进行排名，并观察人类偏见（以排名形式）对学习表示的影响，以进一步改善受监督的对比学习。我们认为这是一个重要的问题，因为学习良好的功能嵌入是在计算机视觉中长期引起的问题。
Contrastive learning is commonly used as a method of self-supervised learning with the "anchor" and "positive" being two random augmentations of a given input image, and the "negative" is the set of all other images. However, the requirement of large batch sizes and memory banks has made it difficult and slow to train. This has motivated the rise of Supervised Contrasative approaches that overcome these problems by using annotated data. We look to further improve supervised contrastive learning by ranking classes based on their similarity, and observe the impact of human bias (in the form of ranking) on the learned representations. We feel this is an important question to address, as learning good feature embeddings has been a long sought after problem in computer vision.
