Food-101 – mining discriminative components with random forests

Bossard L., Guillaumin M., Van Gool L., ''Food-101 – mining discriminative components with random forests'', Lecture notes in computer science, vol. 8694, pp. 446-461, 2014 (13th European conference on computer vision - ECCV 2014, September 6-12, 2014, Zurich, Switzerland).status: publishe

Going Further with Point Pair Features

Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016; https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5

End-to-End Localization and Ranking for Relative Attributes

We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons. Unlike previous methods, our network jointly learns the attribute's features, localization, and ranker. The localization module of our network discovers the most informative image region for the attribute, which is then used by the ranking module to learn a ranking model of the attribute. Our end-to-end framework also significantly speeds up processing and is much faster than previous methods. We show state-of-the-art ranking results on various relative attribute datasets, and our qualitative localization results clearly demonstrate our network's ability to learn meaningful image patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201

SLRTP 2020: The Sign Language Recognition, Translation & Production Workshop

The objective of the “Sign Language Recognition, Translation & Production” (SLRTP 2020) Workshop was to bring together researchers who focus on the various aspects of sign language understanding using tools from computer vision and linguistics. The workshop sought to promote a greater linguistic and historical understanding of sign languages within the computer vision community, to foster new collaborations and to identify the most pressing challenges for the field going forwards. The workshop was held in conjunction with the European Conference on Computer Vision (ECCV), 2020