DMCA
Simultaneous Detection and Segmentation
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BibTeX
@MISC{Hariharan_simultaneousdetection,
author = {Bharath Hariharan and Pablo Arbeláez and Ross Girshick and Jitendra Malik},
title = {Simultaneous Detection and Segmentation},
year = {}
}
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Abstract
Abstract. We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, topdown figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16 % relative) over our baselines on SDS, a 5 point boost (10 % relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.
Keyphrases
simultaneous detection point boost novel architecture future work topdown figure-ground prediction bottom-up proposal diagnostic tool state-of-the-art performance convolutional neural network individual object instance category-independent region proposal classical semantic segmentation classical bounding box detection task simultaneous detection semantic segmentation object detection recent work
