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Found 9 papers, 1 papers with code
Few-Shot Object Detection with Sparse Context Transformers
Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data.
Alternative design of DeepPDNet in the context of image restoration
Preliminary experiments illustrate the good behavior of such a deep primal-dual network in the context of image restoration on BSD68 database.
Image Annotation based on Deep Hierarchical Context Networks
Context modeling is one of the most fertile subfields of visual recognition which aims at designing discriminant image representations while incorporating their intrinsic and extrinsic relationships.
A deep primal-dual proximal network for image restoration
In this work, we design a deep network, named DeepPDNet, built from primal-dual proximal iterations associated with the minimization of a standard penalized likelihood with an analysis prior, allowing us to take advantage of both worlds.
End-to-end training of deep kernel map networks for image classification
Deep kernel map networks have shown excellent performances in various classification problems including image annotation.
Deep Context-Aware Kernel Networks
This architecture is fully determined by the solution of an objective function mixing a content term that captures the intrinsic similarity between data, a context criterion which models their structure and a regularization term that helps designing smooth kernel network representations.
Learning Explicit Deep Representations from Deep Kernel Networks
This scheme has proven to be effective, but intractable when handling large-scale datasets especially when the depth of the trained networks increases; indeed, the complexity of evaluating these networks scales quadratically w. r. t.
Learning Deep Context-Network Architectures for Image Annotation
We apply this context and kernel learning framework to image classification using the challenging ImageCLEF Photo Annotation benchmark; the latter shows that our deep context learning provides highly effective kernels for image classification as corroborated through extensive experiments.
Sparse hierarchical interaction learning with epigraphical projection
This work focuses on learning optimization problems with quadratical interactions between variables, which go beyond the additive models of traditional linear learning.
