Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …

Self-attention based Transformer models have demonstrated impressive results for image
classification and object detection, and more recently for video understanding. Inspired by …

Classification of human actions is an ongoing research problem in computer vision. This
review is aimed to scope current literature on data fusion and action recognition techniques …

Temporal action detection (TAD) aims to determine the semantic label and the temporal
interval of every action instance in an untrimmed video. It is a fundamental and challenging …

Temporal action detection is a fundamental yet challenging task in video understanding.
Video context is a critical cue to effectively detect actions, but current works mainly focus on …

Most modern approaches in temporal action localization divide this problem into two parts:(i)
short-term feature extraction and (ii) long-range temporal boundary localization. Due to the …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Abstract We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm
for online action detection, which employs a long-and short-term memory mechanism to …

Temporal action proposal generation is an important and challenging task in video
understanding, which aims at detecting all temporal segments containing action instances of …

Temporal action proposal generation aims to estimate temporal intervals of actions in
untrimmed videos, which is a challenging yet important task in the video understanding field …