This document discusses using object detection models like Faster R-CNN and Mask R-CNN to extract embeddings from images for image retrieval purposes. It proposes a student-teacher training paradigm where an object detection model acts as the student and learns to transform its output feature map into the feature space of a classification teacher model through knowledge distillation in order to generate more semantically meaningful embeddings. The goal is to perform efficient image retrieval based on these object-level embeddings.

2. Analyzing Embeddings for Image Retrieval
 Faster-RCNN (COCO)
 Faster-RCNN (OpenImagesV4)
 Mask-RCNN (COCO) instance segmentation
 ResNet50 (ImageNet)

3. Analyzing Embeddings for Image Retrieval

4. Analyzing Embeddings for Image Retrieval
PCA Pooling

5. Can Object Detection Help Image Retrieval?

6. Eight bboxs per image object-level embeddings

7. Efficient Image Retrieval using Object Embeddings

8. Student-teacher training paradigm (Knowledge distillation)

9. • Teacher network:
Classification model
Student-teacher training paradigm (Knowledge distillation)
• Object detection model
Student network
Transforms the
feature map from
the object detection
model to the teacher
model

10. Student-teacher training paradigm (Knowledge distillation)

11. Student-teacher training paradigm (Knowledge distillation)

14. Near-Duplicate Object Retrieval

15. THANKS