论文标题
背景可学习的零击对象检测的级联
Background Learnable Cascade for Zero-Shot Object Detection
论文作者
论文摘要
零射击检测（ZSD）对于大规模对象检测至关重要，目的是同时定位和识别看不见的对象。 ZSD仍然存在一些挑战，包括减少背景和看不见的物体之间的歧义，以及改善视觉和语义概念之间的一致性。在这项工作中，我们提出了一个名为背景可学习级联（BLC）的新型框架，以提高ZSD的性能。 BLC的主要贡献如下：（i）我们提出了一个名为CASCADE语义R-CNN的多阶段级联结构，以逐步完善ZSD视觉和语义之间的对齐； （ii）我们开发语义信息流结构，并直接在级联语义rcnn的每个阶段之间添加它，以进一步改善语义特征学习； （iii）我们建议背景可学习的区域建议网络（BLRPN）学习一个适合背景类别的单词向量，并在级联语义上使用该学到的矢量，这种设计使背景使背景可学习”，并减少了背景和不见了的类别之间的混淆。我们的广泛实验显示BLC获得了大量的绩效改进，以改进MS-CocoCoCoCococococococo cococo the-the-the-artsarts。
Zero-shot detection (ZSD) is crucial to large-scale object detection with the aim of simultaneously localizing and recognizing unseen objects. There remain several challenges for ZSD, including reducing the ambiguity between background and unseen objects as well as improving the alignment between visual and semantic concept. In this work, we propose a novel framework named Background Learnable Cascade (BLC) to improve ZSD performance. The major contributions for BLC are as follows: (i) we propose a multi-stage cascade structure named Cascade Semantic R-CNN to progressively refine the alignment between visual and semantic of ZSD; (ii) we develop the semantic information flow structure and directly add it between each stage in Cascade Semantic RCNN to further improve the semantic feature learning; (iii) we propose the background learnable region proposal network (BLRPN) to learn an appropriate word vector for background class and use this learned vector in Cascade Semantic R CNN, this design makes \Background Learnable" and reduces the confusion between background and unseen classes. Our extensive experiments show BLC obtains significantly performance improvements for MS-COCO over state-of-the-art methods.
