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Found 6 papers, 1 papers with code
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images.
Contextual Hourglass Networks for Segmentation and Density Estimation
These shortcut connections improve the performance and it is hypothesized that this is due to mitigating effects on the vanishing gradient problem and the ability of the model to combine feature maps from earlier and later layers.
Learning Short-Cut Connections for Object Counting
Standard short-cut connections are connections between layers in deep neural networks which skip at least one intermediate layer.
The challenge of simultaneous object detection and pose estimation: a comparative study
Detecting objects and estimating their pose remains as one of the major challenges of the computer vision research community.
A Relational-learning Perspective to Multi-label Chest X-ray Classification
Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i. e. learning to map an image directly to its binary labels.
Learning to Transfer with von Neumann Conditional Divergence
Feature similarity includes both the invariance of marginal distributions and the closeness of conditional distributions given the desired response $y$ (e. g., class labels).
