Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering. In recent years, many research efforts have been proposed for knowledge graph embedding (KGE). However, most previous KGE methods ignore the semantic similarity between the related entities and entity-relation couples in different triples since they separately optimize each triple with the scoring function. To address this problem, we propose a simple yet efficient contrastive learning framework for tensor decomposition based (TDB) KGE, which can shorten the semantic distance of the related entities and entity-relation couples in different triples and thus improve the performance of KGE. We evaluate our proposed method on three standard KGE datasets: WN18RR, FB15k-237 and YAGO3-10. Our method can yield some new state-of-the-art results, achieving 51.2% MRR, 46.8% Hits@1 on the WN18RR dataset, 37.8% MRR, 28.6% Hits@1 on FB15k-237 dataset, and 59.1% MRR, 51.8% Hits@1 on the YAGO3-10 dataset.

Skinning using point handles has experimentally shown its effectiveness for stretching, twisting, and supple deformations [Jacobson et al. 2011] which are difficult to achieve using rigid bones. However, point handles are much less effective for limbs bending since their influence weights vary over the skin mesh. This poster presents an efficient scheme, which expands the space of deformations possible to the point handles by supporting rigid bending using a rigidity energy minimization framework. Thus, 3D points that are much easier to design while provide larger space of deformations than a skeleton can be used as alternative handles in character skinning.

This paper presents a lightweight tele-immersive video chat system named CuteChat. Based on our recently developed video object cutout technology, the CuteChat system is designed and optimized to provide a radically new video chat experience by merging each participant in the same shared space, allowing them to interact more naturally in an integrated manner. With the goal to make the system easily accessible by massive consumers, we address the challenges in the whole pipeline of video processing, coding, communication, composition, and playback. Extensive experiments have shown that the proposed CuteChat system runs reliably and comfortably in real time on one's laptop or desktop PC, and it needs only a commodity webcam for video acquisition and just public Internet for tele-immersive video conferencing. With such a really minimal deployment requirement, we present a variety of interesting applications and user experiences created by the CuteChat system.

This paper presents a novel system to explore places of interests based on the large amount of photos that are placed on Google Maps. The system, named ViewFocus, estimates the view directions of photos via robust object matching and camera reconstruction techniques, and geo-registers the directions on the map. Thus users are able to select the places they are interested in, and the system automatically returns a set of precise photos of the target places for users to focus their exploration, by filtering out photos that are pointing to other directions.