Name
Abstract | ||
|---|---|---|
We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this idea as an encoderdecoder convolutional neural network. The network maps an input image of an unknown category and unknown viewpoint to a latent space, from which a deconvolutional decoder can best "lift" the image to its complete viewgrid showing the object from all viewing angles. Our class-agnostic training procedure encourages the representation to capture fundamental shape primitives and semantic regularities in a data-driven mannerwithout manual semantic labels. Our results on two widely-used shape datasets show (1) our approach successfully learns to perform "mental rotation" even for objects unseen during training, and (2) the learned latent space is a powerful representation for object recognition, outperforming several existing unsupervised feature learning methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01270-0_8 | COMPUTER VISION - ECCV 2018, PT XVI |
Keywords | Field | DocType |
Unseen Views,Unsupervised Feature Learning,Mental Rotation,Unknown Viewpoint,ShapeNet | Computer vision,Computer science,Convolutional neural network,Image representation,Artificial intelligence,Feature learning,Cognitive neuroscience of visual object recognition,Mental rotation | Conference |
Volume | ISSN | Citations |
11220 | 0302-9743 | 4 |
PageRank | References | Authors |
0.40 | 39 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dinesh Jayaraman | 1 | 318 | 15.69 |
| Ruohan Gao | 2 | 25 | 5.91 |
| Kristen Grauman | 3 | 6258 | 326.34 |