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.
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.
Next, we show that the association of high-quality segmentations to textual phrases aids in richer semantic understanding and reasoning of these textual phrases.
This paper proposes direct learning of image classification from user-supplied tags, without filtering.
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.