Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 379-384, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-379-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 379-384, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-379-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

SINGLE-SHOT SEMANTIC MATCHER FOR UNSEEN OBJECT DETECTION

V. Gorbatsevich1, Y. Vizilter1, V. Knyaz1,2, and A. Moiseenko1,2 V. Gorbatsevich et al.
  • 1State Research Institute of Aviation System (GosNIIAS), 125319 Moscow, Russia
  • 2Moscow Institute of Physics and Technology (MIPT), Russia

Keywords: Convolutional Neural Networks, Object Detection, Semantic Matching, Single-Shot Detector

Abstract. In this paper we combine the ideas of image matching, object detection, image retrieval and zero-shot learning for stating and solving the semantic matching problem. Semantic matcher takes two images (test and request) as input and returns detected objects (bounding boxes) on test image corresponding to semantic class represented by request (sample) image. We implement our single-shot semantic matcher CNN architecture based on GoogleNet and YOLO/DetectNet architectures. We propose the detection-by-request training and testing protocols for semantic matching algorithms. We train and test our CNN on the ILSVRC 2014 with 200 seen and 90 unseen classes and provide the real-time object detection with mAP 23 for seen and mAP 21 for unseen classes.