Search Results for author:
Found 9 papers, 3 papers with code
Livestock Monitoring with Transformer
Given these challenges, we develop an end-to-end behaviour monitoring system for group-housed pigs to perform simultaneous instance level segmentation, tracking, action recognition and re-identification (STAR) tasks.
Adaptive Neural Message Passing for Inductive Learning on Hypergraphs
Most hypergraph learning approaches convert the hypergraph structure to that of a graph and then deploy existing geometric deep learning methods.
Improving Solar Cell Metallization Designs using Convolutional Neural Networks
Recently, it has been shown that Topology Optimization (TO) can be used to design complex metallization patterns for solar cells that lead to improved performance.
Rotation Equivariant Siamese Networks for Tracking
We further show that this change in orientation can be used to impose an additional motion constraint in Siamese tracking through imposing restriction on the change in orientation between two consecutive frames.
HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs
To model such complex relations, hypergraphs have proven to be a natural representation.
Hard Occlusions in Visual Object Tracking
A tracker is assessed to be good or not based on its performance on the recent tracking datasets, e. g., VOT2019, and LaSOT.
Generating Annotated High-Fidelity Images Containing Multiple Coherent Objects
In particular, layout-to-image generation models have gained significant attention due to their capability to generate realistic complex images containing distinct objects.
Layout-to-Image Generation
Vocal Bursts Intensity Prediction
Fusing Structural and Functional MRIs using Graph Convolutional Networks for Autism Classification
We alleviate the use of such non-imaging metadata and propose a fully imaging-based approach where information from structural and functional Magnetic Resonance Imaging (MRI) data are fused to construct the edges and nodes of the graph.
HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities
Encoding multimedia items into a continuous low-dimensional semantic space such that both types of relations are captured and preserved is extremely challenging, especially if the goal is a unified end-to-end learning framework.
