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Tube-CNN: Modeling temporal evolution of appearance for object detection in video
Tuan-Hung Vu, Anton Osokin, Ivan Laptev
Object detection in video is crucial for many applications. Compared to images, video provides additional cues which can help to disambiguate the detection problem. Our goal in thi…
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models
Tatiana Shpakova, Francis Bach, Anton Osokin
We consider the structured-output prediction problem through probabilistic approaches and generalize the "perturb-and-MAP" framework to more challenging weighted Hamming losses, wh…
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin
We study consistency properties of machine learning methods based on minimizing convex surrogates. We extend the recent framework of Osokin et al. (2017) for the quantitative analy…
Modeling Spatio-Temporal Human Track Structure for Action Localization
Guilhem Chéron, Anton Osokin, Ivan Laptev +1
This paper addresses spatio-temporal localization of human actions in video. In order to localize actions in time, we propose a recurrent localization network (RecLNet) designed to…