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Found 5 papers, 2 papers with code
Learning Furniture Compatibility with Graph Neural Networks
We present two GNN models, both of which comprise a deep CNN that extracts a feature representation for each image, a gated recurrent unit (GRU) network that models interactions between the furniture items in a set, and an aggregation function that calculates the compatibility score.
Deep Adaptive Wavelet Network
Even though convolutional neural networks have become the method of choice in many fields of computer vision, they still lack interpretability and are usually designed manually in a cumbersome trial-and-error process.
Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases
A graph neural network (GNN) for image understanding based on multiple cues is proposed in this paper.
Learning fashion compatibility across apparel categories for outfit recommendation
This paper addresses the problem of generating recommendations for completing the outfit given that a user is interested in a particular apparel item.
Exploiting Restricted Boltzmann Machines and Deep Belief Networks in Compressed Sensing
This paper proposes a CS scheme that exploits the representational power of restricted Boltzmann machines and deep learning architectures to model the prior distribution of the sparsity pattern of signals belonging to the same class.
