744 research outputs found
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
- …