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DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
(Center for Brains, Minds and Machines (CBMM), 2018-06-19)
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 ...
Detecting Semantic Parts on Partially Occluded Objects
(Center for Brains, Minds and Machines (CBMM), 2017-09-04)
In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is ...
Single-Shot Object Detection with Enriched Semantics
(Center for Brains, Minds and Machines (CBMM), 2018-06-19)
We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic ...
Visual concepts and compositional voting
(Center for Brains, Minds and Machines (CBMM), 2018-03-27)
It is very attractive to formulate vision in terms of pattern theory [26], where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is very ...