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The ability of deep neural networks to extract complex statistics and learn high level features from vast datasets is proven.Yet current deep learning approaches suffer from poor sample efficiency in stark contrast to human perception. Fewshot learning algorithms such as matching networks or ModelAgnostic Meta Learning (MAML) mitigate this problem, enabling fast learning with few examples. In this paper, we ex-tend the MAML algorithm to point cloud data using a Point-Net Architecture.

Motions of facial components convey significant information of facial expressions. Although remarkable advancement has been made, the dynamic of facial topology has not been fully exploited. In this paper, a novel facial expression recognition (FER) algorithm called Spatial Temporal Semantic Graph Network (STSGN) is proposed to automatically learn spatial and temporal patterns through end-to-end feature learning from facial topology structure.

The task of head pose estimation from a single depth image is challenging, due to the presence of large pose variations, occlusions and inhomegeneous facial feature space. To solve the problem, we propose Deep Regression Forest with Soft-Attention (SA-DRF) in a multi-task learning setup. It can be integrated with a general feature learning net and jointly learned in an end-to-end manner. The soft-attention module is facilitated to learn soft masks from the general features and feeds the forest with task-specific features to regress head poses.

Light fields are able to capture light rays from a scene arriving at different angles, effectively creating multiple perspective views of the same scene. Thus, one of the flagship applications of light fields is to estimate the captured scene geometry, which can notably be achieved by establishing correspondences between the perspective views, usually in the form of a disparity map. Such correspondence estimation has been a long standing research topic in computer vision, with application to stereo vision or optical flow.