148 papers with code • 18 benchmarks • 21 datasets
Panoptic Segmentation is a computer vision task that combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. The goal of panoptic segmentation is to segment the image into semantically meaningful parts or regions, while also detecting and distinguishing individual instances of objects within those regions.
( Image credit: Detectron2 )
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Most implemented papers
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
End-to-End Object Detection with Transformers
We present a new method that views object detection as a direct set prediction problem.
ResNeSt: Split-Attention Networks
It is well known that featuremap attention and multi-path representation are important for visual recognition.
SOLOv2: Dynamic and Fast Instance Segmentation
Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.
Visual Attention Network
In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings.
PVT v2: Improved Baselines with Pyramid Vision Transformer
We hope this work will facilitate state-of-the-art Transformer researches in computer vision.
Panoptic Feature Pyramid Networks
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.
CenterMask : Real-Time Anchor-Free Instance Segmentation
We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively.
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed.