Facebook

About: Facebook is a company organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Computer science & Artificial neural network. The organization has 7856 authors who have published 10906 publications receiving 570123 citations. The organization is also known as: facebook.com & FB.

Recycle-GAN: Unsupervised Video Retargeting

Abstract: We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver’s speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert’s style. Our approach combines both spatial and temporal information along with adversarial losses for content translation and style preservation. In this work, we first study the advantages of using spatiotemporal constraints over spatial constraints for effective retargeting. We then demonstrate the proposed approach for the problems where information in both space and time matters such as face-to-face translation, flower-to-flower, wind and cloud synthesis, sunrise and sunset.

Consistent video depth estimation

Abstract: We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video. We leverage a conventional structure-from-motion reconstruction to establish geometric constraints on pixels in the video. Unlike the ad-hoc priors in classical reconstruction, we use a learning-based prior, i.e., a convolutional neural network trained for single-image depth estimation. At test time, we fine-tune this network to satisfy the geometric constraints of a particular input video, while retaining its ability to synthesize plausible depth details in parts of the video that are less constrained. We show through quantitative validation that our method achieves higher accuracy and a higher degree of geometric consistency than previous monocular reconstruction methods. Visually, our results appear more stable. Our algorithm is able to handle challenging hand-held captured input videos with a moderate degree of dynamic motion. The improved quality of the reconstruction enables several applications, such as scene reconstruction and advanced video-based visual effects.

Identifying and using identities deemed to be known to a user

Abstract: The people a user is presumed to know or be associated with may be determined using a number of techniques. This information about people that the user knows is used in relation to the user's communications. For example access to a user's online presence may be restricted based on the known people such that access to presence is provided only to those people that the user knows.

Integrated social network environment

Abstract: In one embodiment, a system includes one or more computing systems that implement a social networking environment and are operable to access stored information including a plurality of nodes including a first set of user nodes that each correspond to a respective user and a second set of concept nodes that each correspond to a respective concept. The stored information further includes a plurality of edges that each define a connection between a corresponding pair of nodes including a first set and a second set of edges. Each edge from the first set defining a connection between a pair of user nodes and representing a social relationship between the users corresponding to the user nodes. Each edge from the second set defining a connection between a user node and a concept node and representing an interest of the user of the user node with respect to the corresponding concept node.

Graph R-CNN for Scene Graph Generation

Abstract: We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images. Our model contains a Relation Proposal Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image. We also propose an attentional Graph Convolutional Network (aGCN) that effectively captures contextual information between objects and relations. Finally, we introduce a new evaluation metric that is more holistic and realistic than existing metrics. We report state-of-the-art performance on scene graph generation as evaluated using both existing and our proposed metrics.