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Make One-Shot Video Object Segmentation Efficient Again
TimMeinhardt, LauraLeal-Taixé....
Published date-12/01/2020
ObjectDetection, SemanticSegmentation, VideoObjectSegmentation, VideoSemanticSegmentation, Youtube-VOS
Video object segmentation (VOS) describes the task of segmenting a set of objects in each frame of a video. In the semi-supervised setting, the first mask of each object is …
Counting People by Estimating People Flows
WeizheLiu, MathieuSalzmann, PascalFua....
Published date-12/01/2020
ActiveLearning, CrowdCounting, OpticalFlowEstimation
Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in …
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
JinsungYoon, YaoZhang, JamesJordon....
Published date-12/01/2020
DataAugmentation, Imputation, Self-SupervisedLearning
Self- and semi-supervised learning frameworks have made significant progress in training machine learning models with limited labeled data in image and language domains. These methods heavily rely on the unique …
Learning Disentangled Representations and Group Structure of Dynamical Environments
RobinQuessard, ThomasBarrett, WilliamClements....
Published date-12/01/2020
Learning disentangled representations is a key step towards effectively discovering and modelling the underlying structure of environments. In the natural sciences, physics has found great success by describing the universe …
Inferring learning rules from animal decision-making
ZoeAshwood, NicholasA.Roy, JiHyunBak....
Published date-12/01/2020
DecisionMaking
How do animals learn? This remains an elusive question in neuroscience. Whereas reinforcement learning often focuses on the design of algorithms that enable artificial agents to efficiently learn new tasks, …
Improving model calibration with accuracy versus uncertainty optimization
RanganathKrishnan, OmeshTickoo....
Published date-12/01/2020
ImageClassification, VariationalInference
Obtaining reliable and accurate quantification of uncertainty estimates from deep neural networks is important in safety-critical applications. A well-calibrated model should be accurate when it is certain about its prediction …