Multi-task Learning For Joint Action and Gesture Recognition
This work addresses the need for more efficient and generalizable models in practical computer vision applications by leveraging synergies between closely related tasks, though it is incremental as it applies an existing paradigm to a new combination.
The paper tackled the problem of separate handling of action and gesture recognition in computer vision by proposing a multi-task learning approach that jointly trains a single deep neural network, resulting in better performance for both tasks compared to single-task variants.
In practical applications, computer vision tasks often need to be addressed simultaneously. Multitask learning typically achieves this by jointly training a single deep neural network to learn shared representations, providing efficiency and improving generalization. Although action and gesture recognition are closely related tasks, since they focus on body and hand movements, current state-of-the-art methods handle them separately. In this paper, we show that employing a multi-task learning paradigm for action and gesture recognition results in more efficient, robust and generalizable visual representations, by leveraging the synergies between these tasks. Extensive experiments on multiple action and gesture datasets demonstrate that handling actions and gestures in a single architecture can achieve better performance for both tasks in comparison to their single-task learning variants.