Search Results for author: Mircea Cimpoi

Found 6 papers, 4 papers with code

Neighbourhood Consensus Networks

3 code implementations NeurIPS 2018 Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, Josef Sivic

Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences.

Ranked #2 on Semantic correspondence on PF-PASCAL (PCK (weak) metric)

Semantic correspondence Visual Localization

Deep filter banks for texture recognition, description, and segmentation

no code implementations9 Jul 2015 Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Andrea Vedaldi

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications.

Benchmarking

Deep Filter Banks for Texture Recognition and Segmentation

1 code implementation CVPR 2015 Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.

Material Recognition Scene Recognition

Deep convolutional filter banks for texture recognition and segmentation

no code implementations25 Nov 2014 Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.

Material Recognition Scene Recognition

Describing Textures in the Wild

14 code implementations CVPR 2014 Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, Andrea Vedaldi

Patterns and textures are defining characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly.

Material Recognition Object Recognition

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