论文标题
解决语义混乱以改善零射击检测
Resolving Semantic Confusions for Improved Zero-Shot Detection
论文作者
论文摘要
零射击检测（ZSD）是一项具有挑战性的任务，即使我们的模型未接受几个目标（“看不见”）类的视觉样本培训，我们的目标是同时识别和本地化对象。最近，采用诸如gans之类的生成模型的方法显示了一些最佳结果，其中通过对可见级数据训练的gan来基于其语义生成看不见的样本，从而使香草对象探测器能够识别看不见的对象。但是，语义混乱的问题仍然存在，其中该模型有时无法区分语义相似的类别。在这项工作中，我们建议训练一种生成模型，该模型结合了三胞胎损失，该模型承认班级之间的差异程度，并在生成的样本中反映它们。此外，还强制执行循环一致性损失，以确保类别的类似于其自己的语义的类型。在两个基准ZSD数据集（MSCOCO和PASCAL -VOC）上进行的广泛实验表明，对当前的ZSD方法显示了显着增长，从而减少了语义混乱并改善了看不见的类别的检测。
Zero-shot detection (ZSD) is a challenging task where we aim to recognize and localize objects simultaneously, even when our model has not been trained with visual samples of a few target ("unseen") classes. Recently, methods employing generative models like GANs have shown some of the best results, where unseen-class samples are generated based on their semantics by a GAN trained on seen-class data, enabling vanilla object detectors to recognize unseen objects. However, the problem of semantic confusion still remains, where the model is sometimes unable to distinguish between semantically-similar classes. In this work, we propose to train a generative model incorporating a triplet loss that acknowledges the degree of dissimilarity between classes and reflects them in the generated samples. Moreover, a cyclic-consistency loss is also enforced to ensure that generated visual samples of a class highly correspond to their own semantics. Extensive experiments on two benchmark ZSD datasets - MSCOCO and PASCAL-VOC - demonstrate significant gains over the current ZSD methods, reducing semantic confusion and improving detection for the unseen classes.
