The algorithm and its variants are tested and evaluated within the task of 3D localization of several speakers using both auditory and visual data.
In this work, we focus on the task of learning and representing dense correspondences in deformable object categories.
Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail.
no code implementations • 27 Jan 2020 • Tristan Cazenave, Yen-Chi Chen, Guan-Wei Chen, Shi-Yu Chen, Xian-Dong Chiu, Julien Dehos, Maria Elsa, Qucheng Gong, Hengyuan Hu, Vasil Khalidov, Cheng-Ling Li, Hsin-I Lin, Yu-Jin Lin, Xavier Martinet, Vegard Mella, Jeremy Rapin, Baptiste Roziere, Gabriel Synnaeve, Fabien Teytaud, Olivier Teytaud, Shi-Cheng Ye, Yi-Jun Ye, Shi-Jim Yen, Sergey Zagoruyko
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games.
We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games.