Abstract Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and
localize all action instances in an untrimmed video under only video-level supervision …

With the goal of learning to localize specific object semantics using the low-cost image-level
annotation, weakly supervised object localization (WSOL) has been receiving increasing …

Deep models have the risk of utilizing spurious clues to make predictions, eg, recognizing
actions via classifying the background scene. This problem severely degrades the open-set …

Temporal sentence grounding aims to localize a target segment in an untrimmed video
semantically according to a given sentence query. Most previous works focus on learning …

Abstract Temporal Action Localization (TAL) is an important task of various computer vision
topics such as video understanding, summarization, and analysis. In the real world, the …

We present a method for weakly-supervised action localization based on graph
convolutions. In order to find and classify video time segments that correspond to relevant …

The temporal action localization research aims to discover action instances from untrimmed
videos, representing a fundamental step in the field of intelligent video understanding. With …

Temporal action localization is a challenging task in computer vision, and it tries to find the
start time and the end time of the actions and predict their categories. However, compared to …

Violence detection in videos is very promising in practical applications due to the
emergence of massive videos in recent years. Most previous works define violence …

Temporal sentence grounding (TSG) is an important yet challenging task in video-based
information retrieval. Given an untrimmed video input, it requires the machine to predict the …