@conference {cSalvadorb, title = {Faster R-CNN Features for Instance Search}, booktitle = {CVPR Workshop Deep Vision}, year = {2016}, month = {06/2016}, 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.

2016-05-Seminar-AmaiaSalvador-DeepVision from Image Processing Group on Vimeo.

Convolutional Features for Instance Search from Xavier Giro
}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w12/papers/Salvador_Faster_R-CNN_Features_CVPR_2016_paper.pdf}, author = {Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Satoh, Shin{\textquoteright}ichi} } @conference {cMcGuinnessa, title = {Insight DCU at TRECVID 2015}, booktitle = {TRECVID 2015 Workshop}, year = {2015}, month = {11/2015}, publisher = {NIST}, organization = {NIST}, address = {Gaithersburg, MD, USA}, abstract = {

Insight-DCU participated in the instance search (INS), semantic indexing (SIN), and localization tasks (LOC) this year.

In the INS task we used deep convolutional network features trained on external data and the query data for this year to train our system. We submitted four runs, three based on convolutional network features, and one based on SIFT/BoW. F A insightdcu 1 was an automatic run using features from the last convolutional layer of a deep network with bag-of-words encoding and achieved 0.123 mAP. F A insightdcu 2 modi ed the previous run to use re-ranking based on an R-CNN model and achieved 0.111 mAP. I A insightdcu 3, our interactive run, achieved 0.269 mAP. Our SIFT-based run F A insightdcu 2 used weak geometric consistency to improve performance over the previous year to 0.187 mAP. Overall we found that using features from the convolutional layers improved performance over features from the fully connected layers used in previous years, and that weak geometric consistency improves performance for local feature ranking.

In the SIN task we again used convolutional network features, this time netuning a network pretrained on external data for the task. We submitted four runs, 2C D A insightdcu.15 1..4 varying the top-level learning algorithm and use of concept co-occurance. 2C D A insightdcu.15 1 used a linear SVM top-level learner, and achieved 0.63 mAP. Exploiting concept co-occurance improved the accuracy of our logistic regression run 2C D A insightdcu.15 3 from 0.058 mAP to 0.6 2C D A insightdcu.15 3.

Our LOC system used training data from IACC.1.B and features similar to our INS run, but using a VLAD encoding instead of a bag-of-words. Unfortunately there was problem with the run that we are still investigating.

Note: UPC and NII participated only in the INS task of this submission.

}, url = {http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.15.org.html}, author = {Kevin McGuinness and Mohedano, Eva and Amaia Salvador and Zhang, ZhenXing and Marsden, Mark and Wang, Peng and Jargalsaikhan, Iveel and Antony, Joseph and Xavier Gir{\'o}-i-Nieto and Satoh, Shin{\textquoteright}ichi and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @conference {cNguyen, title = {NII-HITACHI-UIT at TRECVID 2015 Instance Search}, booktitle = {TRECVID 2015 Workshop}, year = {2015}, month = {11/2015}, publisher = {NIST}, organization = {NIST}, address = {Gaithersburg, MD, USA}, abstract = {

In this paper, we propose two methods to improve last year instance search framework. Both of them are based on post processing scheme that try to rerank top K shots returned from BOW model. The rst system is to propose a query-adaptive weighting technique between DPM object detectors score and BOW{\textquoteright}s score. In order to nd a good weight, we use a neural network which learns characteristics of the query including number of features, number of shared words and area of the query topic. The second system combines two state-of-the-art object detectors: DPM and Fast RCNN to estimate object location and similarity score, respectively. The nal score is computed using these components together with BOW based similarity score returned from the baseline system. The experimental results show that our system improved pretty much even with a smaller number of top K input ranked list. Compared to other teams, we got the second place with the same run.

}, url = {http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.15.org.html}, author = {Nguyen, Vinh-Tiep and Duy-Dinh-Le and Amaia Salvador and Caizhi-Zhu and Nguyen, Dinh-Luan and Tran, Minh-Triet and Ngo Duc, Thanh and Anh Duong, Duc and Satoh, Shin{\textquoteright}ichi and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xGarcia-delMolino13, title = {Extension of Instance Search Technique by Geometric Coding and Quantization Error Compensation}, year = {2013}, abstract = {

This MSc thesis analyzes two ways of improving the video retrieval techniques for instance search problem. In one hand, "Pairing Interest Points for a better Signature using Sparse Detector{\textquoteright}s Spatial Information", allows the Bag-of-Words model to keep some spatial information. In the other, "Study of the Hamming Embedding Signature Symmetry in Video Retrieval" provides binary signatures that refine the matching based on visual words, and aims to find the best way of matching taking into account the existent asymmetries between image query and videos.

}, url = {http://hdl.handle.net/2099.1/19159}, author = {Garcia-delMolino, Ana}, editor = {Satoh, Shin{\textquoteright}ichi and Ventura, C. and Xavier Gir{\'o}-i-Nieto} }