DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without seeing occlusions while being able to transfer the learned knowledge to deal with occlusions. This setting alleviates the d...
| Main Authors: | , , , , |
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| Format: | Technical Report |
| Language: | en_US |
| Published: |
Center for Brains, Minds and Machines (CBMM)
2018
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| Online Access: | http://hdl.handle.net/1721.1/115181 |