Due to the large memory footprint of untrimmed videos, current state-of-the-art video
localization methods operate atop precomputed video clip features. These features are …

This paper presents a new large-scale dataset for recognition and temporal localization of
human actions collected from Web videos. We refer to it as HACS (Human Action Clips and …

Temporal action detection is a challenging task due to vagueness of action boundaries. To
tackle this issue, we propose an end-to-end progressive boundary refinement network …

Action localization networks are often structured as a feature encoder sub-network and a
localization sub-network, where the feature encoder learns to transform an input video to …

Motion has shown to be useful for video understanding, where motion is typically
represented by optical flow. However, computing flow from video frames is very …

Despite the recent progress in video understanding and the continuous rate of improvement
in temporal action localization throughout the years, it is still unclear how far (or close?) we …

This paper brings to light one of the most prominent applications of human activity
recognition which is the anomaly detection. Providing security to an individual is a major …

Temporal action detection on unconstrained videos has seen significant research progress
in recent years. Deep learning has achieved enormous success in this direction. However …

In this paper, we address the problem of human activity detection in temporally untrimmed
long video sequences, where the goal is to classify and temporally localize each activity …

Precisely localizing temporal intervals for each action segment in long raw videos is
essential challenge in practical video content analysis (eg, activity detection or video caption …