Search Results for author: Akihiko Torii

Found 12 papers, 5 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

Joint optimization for compressive video sensing and reconstruction under hardware constraints

no code implementations ECCV 2018 Michitaka Yoshida, Akihiko Torii, Masatoshi Okutomi, Kenta Endo, Yukinobu Sugiyama, Rin-ichiro Taniguchi, Hajime Nagahara

Compressive video sensing is the process of encoding multiple sub-frames into a single frame with controlled sensor exposures and reconstructing the sub-frames from the single compressed frame.

Compressive Sensing Frame

Structure-from-Motion using Dense CNN Features with Keypoint Relocalization

no code implementations10 May 2018 Aji Resindra Widya, Akihiko Torii, Masatoshi Okutomi

Then, we demonstrate on the Aachen Day-Night dataset that the proposed SfM using dense CNN features with the keypoint relocalization outperforms a state-of-the-art SfM (COLMAP using RootSIFT) by a large margin.

NetVLAD: CNN architecture for weakly supervised place recognition

15 code implementations CVPR 2016 Relja Arandjelović, Petr Gronat, Akihiko Torii, Tomas Pajdla, Josef Sivic

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph.

Image Retrieval Visual Place Recognition

24/7 Place Recognition by View Synthesis

no code implementations CVPR 2015 Akihiko Torii, Relja Arandjelovic, Josef Sivic, Masatoshi Okutomi, Tomas Pajdla

We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed.

Visual Place Recognition

Visual Place Recognition with Repetitive Structures

no code implementations CVPR 2013 Akihiko Torii, Josef Sivic, Tomas Pajdla, Masatoshi Okutomi

Even more importantly, they violate the feature independence assumed in the bag-of-visual-words representation which often leads to over-counting evidence and significant degradation of retrieval performance.

Visual Place Recognition

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