The idea of semantic segmentation is to recognize and understand what is in an image at the pixel-level.
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We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e. g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video.
By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training and significantly reduces the training memory footprint.
#16 best model for Object Detection 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.
#5 best model for Instance Segmentation on COCO
In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.
SOTA for Object Detection on COCO (using extra training data)
The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects.
#7 best model for Object Detection 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