Kobe University, NICT and University of Siegen at TRECVID 2017 AVS Task

This paper presents the result of the TRECVID 2017 AVS task by kobe nict siegen team. Consisting of three research institutes Kobe University, NICT and University of Siegen. We submitted the following three runs. 1) kobe nict siegen D M 1: This run uses feature vectors extracted by a pre-trained convolutional neural network (CNN) as input for a small-scale multi-layer neural network called micro neural network (microNN). The microNN is a lightweight detector fine-tuned to a target concept using a balanced set of positive and negative data from ImageNet and IACC video datasets. Finally, the results of several microNNs are combined to generate the ranked result. 2) kobe nict siegen D M 2: This run is basically identical to kobe nict siegen D M 1, but applys additional motion features. The motion features are extracted by a motion CNN which involves biologically-inspired motion thresholding and competitive learning. 3) kobe nict siegen D M 3: This run is a degraded version of kobe nict siegen D M 1, and apply fully averaged pooling to feature vectors extracted with CNN, the dimention of the vector is reduced from 3× 3× 2048 to 2048. We also use a word2vec model to find synonyms of concepts in order to improve the performance.