This paper proposes a simple nearest neighbor search algorithm, which provides the exact solution in terms of the Euclidean distance efficiently. Especially, we present an interesting approach to improve the speed of nearest neighbor search by proper translations of data and query although the task is inherently invariant to the Euclidean transformations. The proposed algorithm aims to eliminate nearest neighbor candidates effectively using their distance lower bounds in nonlinear embedded spaces, and further improves the lower bounds by transforming data and query through product quantized translations. Although our framework is composed of simple operations only, it achieves the state-of-the-art performance compared to existing nearest neighbor search techniques, which is illustrated quantitatively using various large-scale benchmark datasets in different sizes and dimensions.

We propose Quadruplet Convolutional Neural Networks (Quad-CNN) for multi-object tracking, which learn to associate object detections across frames using quadruplet losses. The proposed networks consider target appearances together with their temporal adjacencies for data association. Unlike conventional ranking losses, the quadruplet loss enforces an additional constraint that makes temporally adjacent detections more closely located than the ones with large temporal gaps. We also employ a multi-task loss to jointly learn object association and bounding box regression for better localization. The whole network is trained end-to-end. For tracking, the target association is performed by minimax label propagation using the metric learned from the proposed network. We evaluate performance of our multi-object tracking algorithm on public MOT Challenge datasets, and achieve outstanding results.

We propose an image aesthetic quality assessment algorithm, which considers personal taste in addition to generally perceived preference. This problem is formulated by a combination of two different learning frameworks based on support vector machines-Support Vector Regression (SVR) and Ranking SVM (R-SVM), where SVR learns a general model based on public datasets and R-SVM adjusts the model to accommodate personal preference obtained from user interactions. The combined framework, called R-SVR, is represented by a single objective function, which is optimized jointly to learn a model for personalized image aesthetic quality assessment. For the optimization, we use only a small subset of public dataset identified by k-nearest neighbor search instead of using all available training data. This strategy is useful in practice because it reduces training time significantly and alleviates data imbalance problem between regression and ranking. The proposed algorithm is tested through simulation and user study, and we present that our interactive learning algorithm by R-SVR is effective to increase user's satisfaction and improve prediction performance.