Free-Form Region Description with Second-Order Pooling


Semantic segmentation and object detection are today dominated by ways operating on regions obtained as a results of a bottom-up grouping method (segmentation) but use feature extractors developed for recognition on fastened-type (e.g. rectangular) patches, with full images as a special case. This is presumably suboptimal. During this paper we concentrate on feature extraction and outline over free-form regions and study the relationship with their fixed-form counterparts. Our main contributions are novel pooling techniques that capture the second-order statistics of local descriptors within such free-type regions. We have a tendency to introduce second-order generalizations of average and max-pooling that along with applicable non-linearities, derived from the mathematical structure of their embedding space, result in state-of-the-art recognition performance in semantic segmentation experiments while not any type of native feature coding. In distinction, we have a tendency to show that codebook-based mostly local feature coding is a lot of vital when feature extraction is constrained to control over regions that embrace both foreground and massive portions of the background, as typical in image classification settings, whereas for prime-accuracy localization setups, second-order pooling over free-kind regions produces results superior to those of the winning systems in the up to date semantic segmentation challenges, with models that are abundant faster in each training and testing.

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