According to the above finding, this paper proposes a new N8 neighborhood structure considering the movement of critical operations within a critical block and the movement of critical operations outside the critical block.
Our formulation is able to capture global context in a video, thus robust to temporal content change.
The pipeline of our approach starts by reconstructing and refining a 3-D mesh representation of the object of interest from an input image; its control joints are predicted by exploiting the semantic part segmentation information; the obtained object 3-D mesh is then rigged \& animated by non-rigid deformation, and rendered to perform in-situ motions in its original image space.
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 model prolonged sequence data.
However, it is quite expensive to annotate every frame in a large corpus of videos to construct a comprehensive supervised training dataset.
We first introduce the vanilla video transformer and show that transformer module is able to perform spatio-temporal modeling from raw pixels, but with heavy memory usage.
Ranked #3 on Action Classification on Kinetics-700
In this paper, we propose TubeR: the first transformer based network for end-to-end action detection, with an encoder and decoder optimized for modeling action tubes with variable lengths and aspect ratios.
In this work, we focus on improving the inference efficiency of current action recognition backbones on trimmed videos, and illustrate that one action model can also cover then informative region by dropping non-informative features.
While for the online learning algorithm, based on the VR user's actual viewpoint delivered through uplink transmission, we compare it with the predicted viewpoint and update the parameters of the online learning algorithm to further improve the prediction accuracy.
In the world of action recognition research, one primary focus has been on how to construct and train networks to model the spatial-temporal volume of an input video.
Video action recognition is one of the representative tasks for video understanding.
Multi-label activity recognition is designed for recognizing multiple activities that are performed simultaneously or sequentially in each video.
(4) The density near a vortex scales as $r^2$ while the velocity goes as $1/r$, where $r$ is the distance to vortex.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies General Relativity and Quantum Cosmology High Energy Physics - Phenomenology High Energy Physics - Theory
The proposed hybrid attention architecture helps the system focus on learning informative representations for both modality-specific feature extraction and model fusion.
Multimodal affective computing, learning to recognize and interpret human affects and subjective information from multiple data sources, is still challenging because: (i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract level, ignoring time-dependent interactions between modalities.
However, there are two difficulties urgently to be solved for most existing niching metaheuristic algorithms: how to set the optimal values of niching parameters for different optimization problems, and how to jump out of the local optima efficiently.
For the Olympic swimming dataset, our system achieved an accuracy of 88%, an F1-score of 0. 58, a completeness estimation error of 6. 3% and a remaining-time estimation error of 2. 9 minutes.
Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization.
Our system is the first to address the concurrent activity recognition with multisensory data using a single model, which is scalable, simple to train and easy to deploy.