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Found 8 papers, 0 papers with code
Nonprehensile Riemannian Motion Predictive Control
Nonprehensile manipulation involves long horizon underactuated object interactions and physical contact with different objects that can inherently introduce a high degree of uncertainty.
Scene Recomposition by Learning-based ICP
By moving a depth sensor around a room, we compute a 3D CAD model of the environment, capturing the room shape and contents such as chairs, desks, sofas, and tables.
VISER: Visual Self-Regularization
In this work, we propose the use of large set of unlabeled images as a source of regularization data for learning robust visual representation.
Tag Prediction at Flickr: a View from the Darkroom
Our results demonstrate that, for real-world datasets, training exclusively with this noisy data yields performance on par with the standard paradigm of first pre-training on clean data and then fine-tuning.
IM2CAD
Given a single photo of a room and a large database of furniture CAD models, our goal is to reconstruct a scene that is as similar as possible to the scene depicted in the photograph, and composed of objects drawn from the database.
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
Next, we show that the association of high-quality segmentations to textual phrases aids in richer semantic understanding and reasoning of these textual phrases.
Image Classification and Retrieval from User-Supplied Tags
This paper proposes direct learning of image classification from user-supplied tags, without filtering.
Incorporating Scene Context and Object Layout into Appearance Modeling
In this paper, we propose a method to learn scene structures that can encode three main interlacing components of a scene: the scene category, the context-specific appearance of objects, and their layout.
