These models use attention to generate the bag's representation from instances and then train it via bag's classification.
Online temporal action localization from an untrimmed video stream is a challenging problem in computer vision.
First, we propose to predict an actionness score for each video frame.
To alleviate this problem, we add extra constraints to these curves, e. g., the probability of ''action continues'' should be relatively high between probability peaks of ''action starts'' and ''action ends'', so that the entire framework is aware of these latent constraints during an end-to-end optimization process.
To enable research in this direction, we introduce 360Action, the first omnidirectional video dataset for multi-person action recognition.
We present a method for weakly-supervised action localization based on graph convolutions.
The basic foundation of the proposed model is the utilization of motion vectors, which already exist in a compressed video bit stream and provide sufficient information to improve the localization of the target action without requiring high consumption of computational resources compared with other popular methods of extracting motion information, such as optical flows.
In the present study, we developed a novel method, referred to as Gemini Network, for effective modeling of temporal structures and achieving high-performance temporal action localization.
The inconsistent strategy makes it hard to explicitly supervise the action localization model with temporal boundary annotations at training time.