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region refinement

Region refinement is a computer vision technique that adjusts and enhances the boundaries, shapes, or spatial localization of candidate object regions within an image to match the true contours of an object more accurately. In tasks such as object detection and instance segmentation, initial region proposals are often generated through bottom-up methods or generic region generators, which frequently produce coarse boundaries that capture background noise or omit parts of the target instance. Region refinement processes these preliminary proposals using learned models, such as category-specific feature representations, boundary regression networks, or top-down figure-ground segmentation, to iteratively improve localization accuracy and yield precise pixel-level object delineations.

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Simultaneous Detection and Segmentation

Simultaneous Detection and Segmentation

Bharath Hariharan, Pablo Arbeláez, Ross B. Girshick, Jitendra Malik

OrganizationsUniversidad de los AndesUniversity of California Berkeley

Why you should read this

Introduces the simultaneous detection and segmentation framework, extending region-based convolutional networks to detect individual object instances and delineate their exact pixel boundaries.

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, top- down 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.

Added

2026-09-25