Lucid Data Dreaming for Video Object Segmentation

Convolutional networks reach top quality in pixel-level video object segmentation but require a large amount of training data (1k~100k) to deliver such results. We propose a new training strategy which achieves state-of-the-art results across three evaluation datasets while using 20x~1000x less annotated data than competing methods. Our approach is suitable for both single and multiple object segmentation. Instead of using large training sets hoping to generalize across domains, we generate in-domain training data using the provided annotation on the first frame of each video to synthesize ("lucid dream") plausible future video frames. In-domain per-video training data allows us to train high quality appearance- and motion-based models, as well as tune the post-processing stage. This approach allows to reach competitive results even when training from only a single annotated frame, without ImageNet pre-training. Our results indicate that using a larger training set is not automatically better, and that for the video object segmentation task a smaller training set that is closer to the target domain is more effective. This changes the mindset regarding how many training samples and general "objectness" knowledge are required for the video object segmentation task.Comment: Accepted in International Journal of Computer Vision (IJCV

DeMoN: Depth and Motion Network for Learning Monocular Stereo

In this paper we formulate structure from motion as a learning problem. We train a convolutional network end-to-end to compute depth and camera motion from successive, unconstrained image pairs. The architecture is composed of multiple stacked encoder-decoder networks, the core part being an iterative network that is able to improve its own predictions. The network estimates not only depth and motion, but additionally surface normals, optical flow between the images and confidence of the matching. A crucial component of the approach is a training loss based on spatial relative differences. Compared to traditional two-frame structure from motion methods, results are more accurate and more robust. In contrast to the popular depth-from-single-image networks, DeMoN learns the concept of matching and, thus, better generalizes to structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included. Project page: http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation. To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks. Our datasets are the first large-scale datasets to enable training and evaluating scene flow methods. Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.Comment: Includes supplementary materia

Madeira engenheirada na arquitetura brasileira: uma abordagem tectônica

Este trabalho tem a intenção de fomentar a discussão sobre tectônica no campo da arquitetura. Com o crescimento do interesse pela madeira, e a ampliação de novas tecnologias com o material no Brasil, como as madeiras engenheiradas, constatamos que na última década surgiu uma diversidade maior de projetos em madeira do que o Brasil havia apresentado nas décadas anteriores. São nesses projetos que nos apoiamos para fazermos análises sobre a concepção projetual tectônica. Os quatro projetos analisados nos apresentam uma ilustração de um panorama do que vem sendo feito no Brasil, e nos ajudam a jogar luz sobre algumas questões pertinentes a relação da madeira com a arquitetura. Temas como processo construtivo, o uso do material como publicidade, atectônica, pré-fabricação e industrialização são representados por esses projetos. No entanto, a sua contribuição mais pertinente à abordagem tectônica desse trabalho tem relação com uma das definições de Kenneth Frampton para a arquitetura no sentido que expressa o que é a tectônica: o ato de criar e revelar

The novel isoxazoline ectoparasiticide fluralaner: Selective inhibition of arthropod γ-aminobutyric acid- and l-glutamate-gated chloride channels and insecticidal/acaricidal activity

AbstractIsoxazolines are a novel class of parasiticides that are potent inhibitors of γ-aminobutyric acid (GABA)-gated chloride channels (GABACls) and l-glutamate-gated chloride channels (GluCls). In this study, the effects of the isoxazoline drug fluralaner on insect and acarid GABACl (RDL) and GluCl and its parasiticidal potency were investigated. We report the identification and cDNA cloning of Rhipicephalus (R.) microplus RDL and GluCl genes, and their functional expression in Xenopus laevis oocytes. The generation of six clonal HEK293 cell lines expressing Rhipicephalus microplus RDL and GluCl, Ctenocephalides felis RDL-A285 and RDL-S285, as well as Drosophila melanogaster RDLCl-A302 and RDL-S302, combined with the development of a membrane potential fluorescence dye assay allowed the comparison of ion channel inhibition by fluralaner with that of established insecticides addressing RDL and GluCl as targets. In these assays fluralaner was several orders of magnitude more potent than picrotoxinin and dieldrin, and performed 5-236 fold better than fipronil on the arthropod RDLs, while a rat GABACl remained unaffected. Comparative studies showed that R. microplus RDL is 52-fold more sensitive than R. microplus GluCl to fluralaner inhibition, confirming that the GABA-gated chloride channel is the primary target of this new parasiticide. In agreement with the superior RDL on-target activity, fluralaner outperformed dieldrin and fipronil in insecticidal screens on cat fleas (Ctenocephalides felis), yellow fever mosquito larvae (Aedes aegypti) and sheep blowfly larvae (Lucilia cuprina), as well as in acaricidal screens on cattle tick (R. microplus) adult females, brown dog tick (Rhipicephalus sanguineus) adult females and Ornithodoros moubata nymphs. These findings highlight the potential of fluralaner as a novel ectoparasiticide

FlowNet: Learning Optical Flow with Convolutional Networks

Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks where CNNs were successful. In this paper we construct appropriate CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. We propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations. Since existing ground truth data sets are not sufficiently large to train a CNN, we generate a synthetic Flying Chairs dataset. We show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI, achieving competitive accuracy at frame rates of 5 to 10 fps.Comment: Added supplementary materia