论文标题
部分可观测时空混沌系统的无模型预测
Analysis of Visual Reasoning on One-Stage Object Detection
论文作者
论文摘要
当前最新的一阶段对象检测器通过分别处理每个图像区域而无需考虑可能的对象关系而受到限制。这仅仅导致依赖性仅对成功检测对象的高质量卷积特征表示。但是，由于某些具有挑战性的条件，有时可能是不可能的。在本文中，分析了推理特征对单阶段对象检测的使用。我们尝试通过使用自我注意力来推理图像区域的关系的不同架构。 Yolov3-Reasoner2在空间和语义上模型可以增强推理层中的特征，并将其与原始卷积特征融合以提高性能。 Yolov3-Reasoner2模型在MAP上的基线Yolov3相对于基线Yolov3的绝对提高约为2.5％，同时仍在实时运行。
Current state-of-the-art one-stage object detectors are limited by treating each image region separately without considering possible relations of the objects. This causes dependency solely on high-quality convolutional feature representations for detecting objects successfully. However, this may not be possible sometimes due to some challenging conditions. In this paper, the usage of reasoning features on one-stage object detection is analyzed. We attempted different architectures that reason the relations of the image regions by using self-attention. YOLOv3-Reasoner2 model spatially and semantically enhances features in the reasoning layer and fuses them with the original convolutional features to improve performance. The YOLOv3-Reasoner2 model achieves around 2.5% absolute improvement with respect to baseline YOLOv3 on COCO in terms of mAP while still running in real-time.
