Simultaneous Detection and Segmentation
This addresses the problem of precise object localization and segmentation in computer vision, offering a novel approach that combines detection and segmentation for improved accuracy.
The paper tackles the task of Simultaneous Detection and Segmentation (SDS), which involves detecting all instances of a category in an image and segmenting each instance at the pixel level, achieving a 7 point boost (16% relative) over baselines on SDS and state-of-the-art performance in object detection.
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.