# Faster R-CNN Features for Instance Search Source: [Faster R-CNN Features for Instance Search]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:20:33.540Z Authors: Amaia Salvador, Xavier Giro-i-Nieto, Ferran Marques, Shin'ichi Satoh Published: 2016-04-29T16:00:24.000Z ## Abstract Image representations derived from pre-trained Convolutional Neural Networks (CNNs) have become the new state of the art in computer vision tasks such as instance retrieval. This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN. We take advantage of the object proposals learned by a Region Proposal Network (RPN) and their associated CNN features to build an instance search pipeline composed of a first filtering stage followed by a spatial reranking. We further investigate the suitability of Faster R-CNN features when the network is fine-tuned for the same objects one wants to retrieve. We assess the performance of our proposed system with the Oxford Buildings 5k, Paris Buildings 6k and a subset of TRECVid Instance Search 2013, achieving competitive results. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Retrieval on Oxford 5k classic (base)](): Image Retrieval; Oxford 5k classic (base); mAP: 67.9. - [Image Retrieval on Paris 6k classic (base)](): Image Retrieval; Paris 6k classic (base); mAP: 72.9. ## Original sources and implementation [Original paper]() [PDF]()