Visual versus textual embedding for video retrieval

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From an algorithmic point of view, the main idea of relaxation methods to solve (MPCC) is to compute a sequence of stationary points or more precisely approximate stationary points

Linguistic Engineering, Information Retrieval, image retrieval, cross-lingual, content-based retrieval, visual, text-based retrieval, textual, relevance feedback, GIFT, KSite,

Like the continuous variables correspond- ing to the query image visual features, the discrete vari- ables corresponding to the keywords associated to the query images are included

The contributions of this work are three-fold: (1) we formulate the image-based localization problem as a re- gression on an image graph with images as nodes and edges

Figure 4: The 2 strategies used for pattern exploration (a) Exploration of the letter ‘P’ by mean of short scan movements; (b) Exploration o f the letter ‘D’ using first,

and high level visual features, deep learning based action recogni- tion representations (C3D-Preds), and image and video captions for memorability prediction.. The major findings

Also, we compare the neural-network-based document embedding method Doc2Vec with Ariadne in a specific information retrieval use case.. For the word analogy test, the local