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This paper describes two of my best performing approaches on theContent-based Video Relevance Prediction challenge. In the FusedLSTM basedapproach, the inception-pool3 and the C3D-pool5 features are combined using anLSTM and a dense layer to form embeddings with the objective to minimize thetriplet loss function. In the second approach, an Online Kernel SimilarityLearning method is proposed to learn a non-linear similarity measure to adherethe relevance training data. The last section gives a complete comparison ofall the approaches implemented during this challenge, including the onepresented in the baseline paper.

FusedLSTM: Fusing frame-level and video-level features for Content-based Video Relevance Prediction