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UvA-DARE ( Digital Academic Repository ) Attributes Make Sense on Segmented Objects
| Content Provider | Semantic Scholar |
|---|---|
| Author | Mensink, Thomas Pajdla, Tomás Schiele, Bernt Tuytelaars, Tinne |
| Copyright Year | 2014 |
| Abstract | In this paper we aim for object classification and segmentation by attributes. Where existing work considers attributes either for the global image or for the parts of the object, we propose, as our first novelty, to learn and extract attributes on segments containing the entire object. Object-level attributes suffer less from accidental content around the object and accidental image conditions such as partial occlusions, scale changes and viewpoint changes. As our second novelty, we propose joint learning for simultaneous object classification and segment proposal ranking, solely on the basis of attributes. This naturally brings us to our third novelty: object-level attributes for zero-shot, where we use attribute descriptions of unseen classes for localizing their instances in new images and classifying them accordingly. Results on the Caltech UCSD Birds, Leeds Butterflies, and an a-Pascal subset demonstrate that i) extracting attributes on oracle object-level brings substantial benefits ii) our joint learning model leads to accurate attribute-based classification and segmentation, approaching the oracle results and iii) object-level attributes also allow for zero-shot classification and segmentation. We conclude that attributes make sense on segmented objects. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://pure.uva.nl/ws/files/22267149/167626_LiECCV2014.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
