Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category. Some example benchmarks for this task are Cityscapes, PASCAL VOC and ADE20K. Models are usually evaluated with the Mean Intersection-Over-Union (Mean IoU) and Pixel Accuracy metrics.
( Image credit: CSAILVision )
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We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.
Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.
Ranked #1 on Semi-Supervised Video Object Segmentation on YouTube
Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space.
Ranked #7 on Semantic Segmentation on PASCAL VOC 2012 val
Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.
Ranked #3 on Human Part Segmentation on PASCAL-Part
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Ranked #1 on Real-Time Object Detection on COCO minival (MAP metric)
3D INSTANCE SEGMENTATION HUMAN PART SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING MULTI-PERSON POSE ESTIMATION MULTI-TISSUE NUCLEUS SEGMENTATION NUCLEAR SEGMENTATION PANOPTIC SEGMENTATION REAL-TIME OBJECT DETECTION
In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks.
Ranked #4 on Panoptic Segmentation on KITTI Panoptic Segmentation
The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e. g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression.