241 citations · 615 across the 38 of their papers we have counts for
7 papers · 1 filter
Image Reassembly Combining Deep Learning and Shortest Path Problem
M. -M. Paumard, D. Picard, H. Tabia
This paper addresses the problem of reassembling images from disjointed fragments. More specifically, given an unordered set of fragments, we aim at reassembling one or several pos…
Jigsaw Puzzle Solving Using Local Feature Co-Occurrences in Deep Neural Networks
Marie-Morgane Paumard, David Picard, Hedi Tabia
Archaeologists are in dire need of automated object reconstruction methods. Fragments reassembly is close to puzzle problems, which may be solved by computer vision algorithms. As…
Leveraging Implicit Spatial Information in Global Features for Image Retrieval
Pierre Jacob, David Picard, Aymeric Histace +1
Most image retrieval methods use global features that aggregate local distinctive patterns into a single representation. However, the aggregation process destroys the relative spat…
Images & Recipes: Retrieval in the cooking context
Micael Carvalho, Rémi Cadène, David Picard +2
Recent advances in the machine learning community allowed different use cases to emerge, as its association to domains like cooking which created the computational cuisine. In this…
Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings
Micael Carvalho, Rémi Cadène, David Picard +3
Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning…
GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange
Michael Blot, David Picard, Matthieu Cord
We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent. Our parallel optimization…