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Temporal Action localization is a more challenging vision task than action recognition because videos to be analyzed

In this work, we propose a new variational blind deconvolution method for spike and slab prior models. Soft-sparse or shrinkage priors such as the Laplace and other related Gaussian Scale Mixture priors may not be ideal sparsity promoting priors. They assign zero probability mass to events we may be interested in assigning a probability greater than zero. The truly sparse nature of the spike and slab priors allows us to discard irrelevant information in the blur estimation process, resulting in improved performance.

Although promising results have been achieved in the areas of object detection and classification, few works have provided an end-to-end solution to the perception problems in the autonomous driving field. In this paper, we make two contributions. Firstly, we fully enhanced our previously released TT100K benchmark and provide 16,817 elaborately labeled Tencent Street View panoramas.

Affect-Sensitive Human-Computer Interaction is enjoying growing attention. Emotions are an essential part of interaction, whether it is between humans or human and machine. This paper analyses the interaction of a user with four different virtual avatars, each manifesting distinct emotional displays, based on the principles of Affect Control Theory. Facial expressions are represented as a vector in a 3D continuous space and different sets of static visual features are evaluated for facial expression recognition.

A structure-adaptive vector median filter (SAVMF) for removal of impulse noise from color images is presented in this paper. A color image is represented in quaternion form, and then quaternion Fourier transform is employed to detect the dominant orientation of the pattern in a local neighborhood. Based on the local orientation and its strength, the size, shape and orientation of the support window of vector median filter (VMF) can be adaptively computed, and thus structure-adaptive VMF is implemented.