1,732 research outputs found
Scaling Out-of-Distribution Detection for Real-World Settings
Detecting out-of-distribution examples is important for safety-critical
machine learning applications such as medical screening and self-driving cars.
However, existing research mainly focuses on simple small-scale settings. To
set the stage for more realistic out-of-distribution detection, we depart from
small-scale settings and explore large-scale multiclass and multi-label
settings with high-resolution images and hundreds of classes. To make future
work in real-world settings possible, we also create a new benchmark for
anomaly segmentation by introducing the Combined Anomalous Object Segmentation
benchmark. Our novel benchmark combines two datasets for anomaly segmentation
that incorporate both realism and anomaly diversity. Using both real images and
those from a simulated driving environment, we ensure the background context
and a wide variety of anomalous objects are naturally integrated, unlike
before. We conduct extensive experiments in these more realistic settings for
out-of-distribution detection and find that a surprisingly simple detector
based on the maximum logit outperforms prior methods in all the large-scale
multi-class, multi-label, and segmentation tasks we consider, establishing a
new baseline for future work. These results, along with our new anomaly
segmentation benchmark, open the door to future research in out-of-distribution
detection.Comment: StreetHazards dataset and code are available at
https://github.com/hendrycks/anomaly-se
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning
Recent work about synthetic indoor datasets from perspective views has shown
significant improvements of object detection results with Convolutional Neural
Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale
indoor dataset containing 100,000 high-resolution diversified fisheye images
with 14 classes. To this end, we create 3D virtual environments of living
rooms, different human characters and interior textures. Beside capturing
fisheye images from virtual environments we create annotations for semantic
segmentation, instance masks and bounding boxes for object detection tasks. We
compare our synthetic dataset to state of the art real-world datasets for
omnidirectional images. Based on MS COCO weights, we show that our dataset is
well suited for fine-tuning CNNs for object detection. Through a high
generalization of our models by means of image synthesis and domain
randomization, we reach an AP up to 0.84 for class person on High-Definition
Analytics dataset.Comment: Paper accepted in WACV 202
The fourth phase of the radiative transfer model intercomparison (RAMI) exercise : Actual canopy scenarios and conformity testing
The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RI model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals. (C) 2015 The Authors. Published by Elsevier Inc.Peer reviewe
G-CSC Report 2010
The present report gives a short summary of the research of the Goethe Center for Scientific Computing (G-CSC) of the Goethe University Frankfurt. G-CSC aims at developing and applying methods and tools for modelling and numerical simulation of problems from empirical science and technology. In particular, fast solvers for partial differential equations (i.e. pde) such as robust, parallel, and adaptive multigrid methods and numerical methods for stochastic differential equations are developed. These methods are highly adanvced and allow to solve complex problems..
The G-CSC is organised in departments and interdisciplinary research groups. Departments are localised directly at the G-CSC, while the task of interdisciplinary research groups is to bridge disciplines and to bring scientists form different departments together. Currently, G-CSC consists of the department Simulation and Modelling and the interdisciplinary research group Computational Finance
Rain rendering for evaluating and improving robustness to bad weather
Rain fills the atmosphere with water particles, which breaks the common
assumption that light travels unaltered from the scene to the camera. While it
is well-known that rain affects computer vision algorithms, quantifying its
impact is difficult. In this context, we present a rain rendering pipeline that
enables the systematic evaluation of common computer vision algorithms to
controlled amounts of rain. We present three different ways to add synthetic
rain to existing images datasets: completely physic-based; completely
data-driven; and a combination of both. The physic-based rain augmentation
combines a physical particle simulator and accurate rain photometric modeling.
We validate our rendering methods with a user study, demonstrating our rain is
judged as much as 73% more realistic than the state-of-theart. Using our
generated rain-augmented KITTI, Cityscapes, and nuScenes datasets, we conduct a
thorough evaluation of object detection, semantic segmentation, and depth
estimation algorithms and show that their performance decreases in degraded
weather, on the order of 15% for object detection, 60% for semantic
segmentation, and 6-fold increase in depth estimation error. Finetuning on our
augmented synthetic data results in improvements of 21% on object detection,
37% on semantic segmentation, and 8% on depth estimation.Comment: 19 pages, 19 figures, IJCV 2020 preprint. arXiv admin note: text
overlap with arXiv:1908.1033
Proceedings of the 2019 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
In 2019 fand wieder der jährliche Workshop des Fraunhofer IOSB und des Lehrstuhls für Interaktive Echtzeitsysteme des Karlsruher Insitut für Technologie statt. Die Doktoranden beider Institutionen präsentierten den Fortschritt ihrer Forschung in den Themen Maschinelles Lernen, Machine Vision, Messtechnik, Netzwerksicherheit und Usage Control. Die Ideen dieses Workshops sind in diesem Buch gesammelt in der Form technischer Berichte
Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor
We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments. Existing methods typically fail for hand-object interactions in cluttered scenes imaged from egocentric viewpoints, common for virtual or augmented reality applications. Our approach uses two subsequently applied Convolutional Neural Networks (CNNs) to localize the hand and regress 3D joint locations. Hand localization is achieved by using a CNN to estimate the 2D position of the hand center in the input, even in the presence of clutter and occlusions. The localized hand position, together with the corresponding input depth value, is used to generate a normalized cropped image that is fed into a second CNN to regress relative 3D hand joint locations in real time. For added accuracy, robustness and temporal stability, we refine the pose estimates using a kinematic pose tracking energy. To train the CNNs, we introduce a new photorealistic dataset that uses a merged reality approach to capture and synthesize large amounts of annotated data of natural hand interaction in cluttered scenes. Through quantitative and qualitative evaluation, we show that our method is robust to self-occlusion and occlusions by objects, particularly in moving egocentric perspectives
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