Topic: Progress on CBMM Challenge
We continue the series of weekly discussions and reports on each CBMM challenge question describing progress and problems of ongoing work at CBMM.
Thrust 5 is focused on models for the CBMM challenge that can answer CBMM challenge questions while being consistent with human behavior and neural data. This talk presents three recent studies on detecting and parsing objects and scenes and discusses how they contribute to the CBMM challenge. We first address the “what?” problem of detecting animals and animal parts (in a newly labelled dataset) and show the advantages of part-sharing (X. Chen et al. 2014). Next, within the same “what?” problem, we describe an approach to parse humans and estimate their three-dimensional structure from single images (C. Chen et al. CVPR 2014). Finally, we describe “psychophysics in the wild” for rapid detection of objects in complex scenes in a newly labelled dataset (Y. Li et al, CVPR 2014). We conclude discussing how these approaches should be extended to meet the CBMM challenge and other efforts at CBMM.
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