GT+s-SVM: for ImageNet attributes, there is not enough data to learn 172 strong DPM detectors as in aPascal So we use the ground truth bounding box instead
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[PDF] Attribute learning in large-scale datasets - Stanford AI Lab
We then learn 20 visual attributes on the ImageNet data and present results on a number of tasks in Section 5 We conclude and discuss future work in Section 6 2
[PDF] Models of Semantic Representation with Visual Attributes
or from ImageNet (Deng et al , 2009), e g , by sual attributes instead of low- level image fea- tures database of images and their visual attributes) and
[PDF] Automatic Discovery, Association Estimation - CVF Open Access
enable attribute-based learning on ImageNet and will share the attributes and associations for future research 1 Introduction Semantic attributes, being both
[PDF] Inferring Analogous Attributes - UT Austin Computer Science
We validate our approach on two large-scale attribute datasets, SUN [17] and ImageNet [19], to explore both object-sensitive and scene-sensitive attributes We
Weakly supervised learning of objects, attributes and their - CORE
GT+s-SVM: for ImageNet attributes, there is not enough data to learn 172 strong DPM detectors as in aPascal So we use the ground truth bounding box instead
Learning Deep Representation with Large-Scale Attributes
contains rich attribute annotations (over 300 attributes) for ∼180k samples and 494 object classes Based on the ImageNet object detection dataset,
[PDF] Automatic Gating of Attributes in Deep Structure - IJCAI
Experimental results on a manually-labeled subset of ImageNet, a-Yahoo and a- Pascal data set justify the superior- ity of AG-DBN against several baselines
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