Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks by simply operating on graph-smoothed node features, rather than using end-to-end learned feature hierarchies that are challenging to scale to large graphs… In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between […]

Conventional unsupervised multi-source domain adaptation(UMDA) methods assume all source domains can be accessed directly. This neglects the privacy-preserving policy, that is,all the data and computations must be kept decentralized.There exists three problems in this scenario: (1)Minimizing the domain distance requires the pairwise calculation of the data from source and target domains, which is not accessible… (2)The communication cost and privacy security limit the application of UMDA methods (e.g.,the domain adversarial training). (3)Since users have no authority to checkthe data quality, […]

In this paper, we address the problem of estimating dense depth from a sequence of images using deep neural networks. Specifically, we employ a dense-optical-flow network to compute correspondences and then triangulate the point cloud to obtain an initial depth map… Parts of the point cloud, however, may be less accurate than others due to lack of common observations or small baseline-to-depth ratio. To further increase the triangulation accuracy, we introduce a depth-refinement network (DRN) that optimizes the initial depth […]

A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed as the brand personality of the company. The perception is impressed upon the consumer through the content, be it in the form of advertisement, blogs or magazines, produced by the organization… A consistent brand will generate trust and retain customers over time as they develop an affinity towards regularity and common patterns. However, maintaining a consistent messaging tone for a brand has […]

Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc. Differing from the dominant regression-based approaches for orientation estimation, this paper explores a relatively less-studied methodology based on classification… The hope is to inherently dismiss the boundary discontinuity issue as encountered by the regression-based detectors. We propose new techniques to push its frontier in two aspects: i) new encoding mechanism: the design of two Densely Coded Labels (DCL) for angle […]

Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the graph structure and node features by aggregating and transforming information within node neighborhoods… However, through theoretical and empirical analysis, we reveal that the aggregation process of GNNs tends to destroy node similarity in the original feature space. There are many scenarios where node similarity plays a crucial role. Thus, it has motivated the proposed framework SimP-GCN […]

Relation extraction is the task of identifying relation instance between two entities given a corpus whereas Knowledge base modeling is the task of representing a knowledge base, in terms of relations between entities. This paper proposes an architecture for the relation extraction task that integrates semantic information with knowledge base modeling in a novel manner… Existing approaches for relation extraction either do not utilize knowledge base modelling or use separately trained KB models for the RE task. We present a […]

Sketching or doodling is a popular creative activity that people engage in. However, most existing work in automatic sketch understanding or generation has focused on sketches that are quite mundane… In this work, we introduce two datasets of creative sketches — Creative Birds and Creative Creatures — containing 10k sketches each along with part annotations. We propose DoodlerGAN — a part-based Generative Adversarial Network (GAN) — to generate unseen compositions of novel part appearances. Quantitative evaluations as well as human […]