Occlusion-Aware Object Localization, Segmentation and Pose Estimation

We present a learning approach for localization and segmentation of objects in an image in a manner that is robust to partial occlusion. Our algorithm produces a bounding box around the full extent of the object and labels pixels in the interior that belong to the object. Like existing segmentation aware detection approaches, we learn an appearance model of the object and consider regions that do not fit this model as potential occlusions. However, in addition to the established use of pairwise potentials for encouraging local consistency, we use higher order potentials which capture information at the level of im- age segments. We also propose an efficient loss function that targets both localization and segmentation performance. Our algorithm achieves 13.52% segmentation error and 0.81 area under the false-positive per image vs. recall curve on average over the challenging CMU Kitchen Occlusion Dataset. This is a 42.44% decrease in segmentation error and a 16.13% increase in localization performance compared to the state-of-the-art. Finally, we show that the visibility labelling produced by our algorithm can make full 3D pose estimation from a single image robust to occlusion.Comment: British Machine Vision Conference 2015 (poster

Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving. A deep predictive model is trained to anticipate imminent accidents from traditional video streams. In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations. In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about the uncertainty inherent to the task. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.Comment: 8 pages, 4 figure

Défigement et traduction intralinguale et interlinguale

La reformulation, au même titre que la recherche d’équivalents de séquences figées, pose des problèmes. L’un des recours linguistiques sollicité dans ces cas est le défigement en ce sens qu’il ouvre des paradigmes qui favorisent justement le « dire autrement ».En fait, chaque mot construit et chaque unité polylexicale offrent un dédoublement potentiel par voie de défigement. L’économie du défigement permet, entre autres, à ce procédé d’agir en remontant à chaque fois à l’encodage de l’expression pour en « déverrouiller » les items lexicaux.Dans la pratique de la traduction intra- et interlinguale, le rendement du défigement se vérifie dans des domaines aussi différents que ceux de l’étymologie populaire, de la paraphrase et du jeu de mots par défigement ludique.The reformulation as much as the search for equivalents of frozen sequences poses certain problems. The defrosting is a linguistic recourse, solicited in this case, because it opens paradigms that rightly favour the “saying otherwise.” In fact, every constructed word and polylexical unit offers a potential doubling by means of defrosting. The economy of defrosting enables, among other elements, to act by going back each time to the encoding of the expression so as to “unlock” the lexical items.In the practice of intra and interlingual translation, the productivity of defrosting is checked in fields as varied as those of popular etymology, of the paraphrase and the pun on words through ludic defrosting