Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.
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Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
#2 best model for Multi-Human Parsing on MHP v1.0
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
#2 best model for Instance Segmentation on COCO
We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
#2 best model for Image-to-Image Translation on ADE20K-Outdoor Labels-to-Photos
The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects.
#5 best model for Object Detection on COCO
To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.
#5 best model for Instance Segmentation on COCO
In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks.
#3 best model for Instance Segmentation on COCO
We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information.
By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation.
#3 best model for Lane Detection on TuSimple