The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1. 58M action labels with multiple labels per person occurring frequently.
#2 best model for Temporal Action Localization on J-HMDB-21
YOWO is a single-stage architecture with two branches to extract temporal and spatial information concurrently and predict bounding boxes and action probabilities directly from video clips in one evaluation.
To address this challenging issue, we exploit the effectiveness of deep networks in temporal action localization via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes on the learned classification network to localize each action instance.
Then we apply the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.
This paper presents a new large-scale dataset for recognition and temporal localization of human actions collected from Web videos.
In this work, we first identify two underexplored problems posed by the weak supervision for temporal action localization, namely action completeness modeling and action-context separation.
#2 best model for Weakly Supervised Action Localization on ActivityNet-1.3
In this report, we introduce the Winner method for HACS Temporal Action Localization Challenge 2019.
Temporal Action Proposal (TAP) generation is an important problem, as fast and accurate extraction of semantically important (e. g. human actions) segments from untrimmed videos is an important step for large-scale video analysis.
#9 best model for Action Recognition In Videos on THUMOS’14
This formulation does not fully model the problem in that background frames are forced to be misclassified as action classes to predict video-level labels accurately.
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.