Object proposal generation is a preprocessing technique that has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images.
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We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components.
To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features.
SOTA for Edge Detection on SBD
We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.
#2 best model for Object Proposal Generation on PASCAL VOC 2012, 60 proposals per image
Generic object detection, aiming at locating object instances from a large number of predefined categories in natural images, is one of the most fundamental and challenging problems in computer vision.