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Structured output regression for detection with partial truncation
Andrea Vedaldi · Andrew Zisserman

Mon Dec 07 07:00 PM -- 11:59 PM (PST) @

We develop a structured output model for object category detection that explicitly accounts for alignment, multiple aspects and partial truncation in both training and inference. The model is formulated as large margin learning with latent variables and slack rescaling, and both training and inference are computationally efficient. We make the following contributions: (i) we note that extending the Structured Output Regression formulation of Blaschko and Lampert (ECCV 2008) to include a bias term significantly improves performance; (ii) that alignment (to account for small rotations and anisotropic scalings) can be included as a latent variable and efficiently determined and implemented; (iii) that the latent variable extends to multiple aspects (e.g. left facing, right facing, front) with the same formulation; and (iv), most significantly for performance, that truncated and truncated instances can be included in both training and inference with an explicit truncation mask. We demonstrate the method by training and testing on the PASCAL VOC 2007 data set -- training includes the truncated examples, and in testing object instances are detected at multiple scales, alignments, and with significant truncations.

Author Information

Andrea Vedaldi (Facebook AI Research and University of Oxford)
Andrew Zisserman (DeepMind & University of Oxford)

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