iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects (2018, methodology): “Previous research has successfully used 3D graphics as a diagnostic tool for computer vision systems [7, 31, 47, 32, 50].”
CORe50: a New Dataset and Benchmark for Continuous Object Recognition (2017, neutral): “NORB [12] 5 25 20 20* grayscale turntable no COIL-100 [18] - 100 20 54* RGB turntable no iLab-20M [2] 15 704 - RGB turntable no RGB-D [24] 51 300 - RGB-D turntable no BigBIRD [25] - 100 - RGB-D turntable no ALOI [8] - 1000 - RGB turntable no BigBrother [6] - 7 54 ∼20 RGB wall cameras no iCubWorld28 ”
Active Long Term Memory Networks (2016, methodology): “udy Deep Linear Networks learning (DLNs) dynamics, to generate a synthetic dataset with a hierarchical structure similar to the taxonomy of categories typical to the domain of living things. iLab20M [3]: in this highly controlled dataset, images of toy-vehicles are collected over a turntable with mu”
CURE-OR: Challenging Unreal and Real Environments for Object Recognition (2018, background): “Among all aforementioned controlled datasets, iLab-20M [29] is the largest”
AGA: Attribute-Guided Augmentation (2016, background): “, we have one interval [l, h] = [1, 2] and one attribute (depth) with target value 3[m].”
ObjectNet Dataset: Reanalysis and Correction (2020, unknown): “ImageNet [6], CIFAR [7], NORB [8], iLab20M [9]).”