Moment Localization Using Multi-Scale 2D Temporal Adjacent Networks and Natural Language

Mahesh Tunguturi

Abstract


We use natural language to solve the challenge of obtaining a specific instant from an untrimmed movie. It is a difficult problem to solve because a target instant may occur in the context of other temporal occurrences in the untrimmed movie. Existing approaches cannot adequately address this difficulty because they do not take into account the temporal circumstances between temporal events. In this research, we use a collection of preset two-dimensional maps with varying temporal scales to characterise the temporal context between video instances. One dimension of each map denotes the beginning time of an instant, while the other indicates the length. These 2D temporal maps may encompass a wide range of video moments of varying durations while describing their neighbouring contexts at various temporal scales.

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