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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'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.
CitationNguyen, V., Le, D., Salvador, A., Zu, C., Nguyen, D., Tran, M., Duc, T., Duong, D., Satoh, S., Giro, X. NII-HITACHI-UIT at TRECVID 2015 instance search. A: TRECVID Workshop. "TRECVID 2015 Overview Papers and Slides". Gaithersburg, MD: 2015, p. 1-17.
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