Weakly supervised scale-invariant learning of models for visual recognition
We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning is translation and scale-invariant; does not require alignment or correspondence between the training images, and is rob...
| Main Authors: | , , |
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| 格式: | Journal article |
| 語言: | English |
| 出版: |
Springer Nature
2006
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