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Faster R-CNN Features for Instance Search Amaia Salvador, Xavier Giró, Ferran Marqués, Shin’ichi Satoh [1] Ren, S., He, K., Girshick, R., & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS 2015 Motivation ● Both global and local features can be extracted in a single forward pass from a pre-trained CNN for object detection. ● Suitable for fast retrieval and spatial reranking. This work has been developed in the framework of the project BigGraph TEC2013-43935-R, funded by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). References WiCV 2016 Acknowledgements Paris Buildings TRECVID Instance Search 2013 (subset of 23k frames) Oxford Buildings Faster R-CNN Image- and region-wise descriptors are extracted from the pre-trained Faster R-CNN model [1]. Spatial Reranking Two different strategies for spatial reranking are explored, using pre-trained and fine-tuned Faster R-CNN models: Fine-tuning for Query Objects We fine tune Faster R-CNN to detect query objects, using query images as training data. We train two models, one updating all layers (ft#2), and one updating only fully connected ones (ft#1). Class-Specific Spatial Reranking (CS-SR) Class-Agnostic Spatial Reranking (CA-SR) Results Spatial reranking improves the retrieval baseline, and provides object localization: Representation Query image Matching Ranklist Image Database v = (v 1 , …, v n ) v 1 = (v 11 , …, v 1n ) v k = (v k1 , …, v kn ) Similarity Metric ... ... Spatial Reranking Find code, slides & video at: http://imatge-upc.github.io/retrieval-2016-deepvision Datasets

k1 Faster R-CNN Features for Instance Search · 2016 Acknowledgements Paris Buildings TRECVID Instance Search 2013 (subset of 23k frames) Oxford Buildings Faster R-CNN Image- and

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Page 1: k1 Faster R-CNN Features for Instance Search · 2016 Acknowledgements Paris Buildings TRECVID Instance Search 2013 (subset of 23k frames) Oxford Buildings Faster R-CNN Image- and

Faster R-CNN Features for Instance SearchAmaia Salvador, Xavier Giró, Ferran Marqués, Shin’ichi Satoh

[1] Ren, S., He, K., Girshick, R., & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS 2015

Motivation

● Both global and local features can be extracted in

a single forward pass from a pre-trained CNN for

object detection.

● Suitable for fast retrieval and spatial reranking.

This work has been developed in the framework of the project BigGraph TEC2013-43935-R, funded by the

Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF).

ReferencesWiCV 2016

Acknowledgements

Paris Buildings

TRECVID Instance Search 2013(subset of 23k frames)

Oxford Buildings

Faster R-CNN

Image- and region-wise descriptors are extracted

from the pre-trained Faster R-CNN model [1].

Spatial Reranking

Two different strategies for spatial reranking are explored, using pre-trained and fine-tuned Faster R-CNN models:

Fine-tuning for Query Objects

We fine tune Faster R-CNN to detect query objects, using

query images as training data. We train two models, one

updating all layers (ft#2), and one updating only fully

connected ones (ft#1).

Class-Specific Spatial Reranking (CS-SR)

Class-Agnostic Spatial Reranking (CA-SR)

Results

Spatial reranking improves the retrieval

baseline, and provides object localization:

RepresentationQuery image Matching Ranklist

Image Database

v = (v1

, …, vn)

v1

= (v11

, …, v1n

)

vk = (v

k1, …, v

kn)

Sim

ilari

ty M

etri

c

...

...

Spat

ial R

eran

kin

g

Find code, slides & video at: http://imatge-upc.github.io/retrieval-2016-deepvision

Datasets

http://imatge-upc.github.io/retrieval-2016-deepvision/
http://imatge-upc.github.io/retrieval-2016-deepvision/

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