Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

Media is created by humans for humans to tell stories. There exists a natural and imminent
need for creating human-centered media analytics to illuminate the stories being told and to …

Machine learning is useful for grid-based crime prediction. Many previous studies have
examined factors including time, space, and type of crime, but the geographic characteristics …

The task of predicting a movie genre from its poster can be very challenging owing to the
high variability of movie posters. A novel approach for the generation of a multi-label movie …

Affective video content analysis has emerged as one of the most challenging and essential
research tasks as it aims to analyze the emotions elicited by videos automatically. However …

Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as
visual voice clone, V2C) task aims to generate speeches that match the speaker's emotion …

Feature selection is a crucial pre-processing step for learning tasks to mitigate the “curse of
dimensionality”, which is caused by irrelevant and redundant features in high-dimensional …

Feature selection, as an important pre-processing technique, can efficiently mitigate the
issue of “the curse of dimensionality” by selecting discriminative features especially for multi …

Movie genre classification is a challenging task that has increasingly attracted the attention
of researchers. The number of movie consumers interested in taking advantage of automatic …

In this paper, we study the transferability of ImageNet spatial and Kinetics spatio-temporal
representations to multi-label Movie Trailer Genre Classification (MTGC). In particular, we …