PointINS: Point-based Instance Segmentation

• URL: http://arxiv.org/abs/2003.06148v2
• Date: Thu, 1 Jul 2021 09:07:06 GMT
• Title: PointINS: Point-based Instance Segmentation
• Authors: Lu Qi and Yi Wang and Yukang Chen and Yingcong Chen and Xiangyu Zhang and Jian Sun and Jiaya Jia
• Abstract summary: Mask representation in instance segmentation with Point-of-Interest (PoI) features is challenging because learning a high-dimensional mask feature for each instance requires a heavy computing burden. We propose an instance-aware convolution, which decomposes this mask representation learning task into two tractable modules. Along with instance-aware convolution, we propose PointINS, a simple and practical instance segmentation approach.
• Score: 117.38579097923052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features. Differentiating multiple potential instances within a single PoI feature is challenging because learning a high-dimensional mask feature for each instance using vanilla convolution demands a heavy computing burden. To address this challenge, we propose an instance-aware convolution. It decomposes this mask representation learning task into two tractable modules as instance-aware weights and instance-agnostic features. The former is to parametrize convolution for producing mask features corresponding to different instances, improving mask learning efficiency by avoiding employing several independent convolutions. Meanwhile, the latter serves as mask templates in a single point. Together, instance-aware mask features are computed by convolving the template with dynamic weights, used for the mask prediction. Along with instance-aware convolution, we propose PointINS, a simple and practical instance segmentation approach, building upon dense one-stage detectors. Through extensive experiments, we evaluated the effectiveness of our framework built upon RetinaNet and FCOS. PointINS in ResNet101 backbone achieves a 38.3 mask mean average precision (mAP) on COCO dataset, outperforming existing point-based methods by a large margin. It gives a comparable performance to the region-based Mask R-CNN with faster inference.

Related papers

• Bridge the Points: Graph-based Few-shot Segment Anything Semantically [79.1519244940518]
  Recent advancements in pre-training techniques have enhanced the capabilities of vision foundation models. Recent studies extend the SAM to Few-shot Semantic segmentation (FSS) We propose a simple yet effective approach based on graph analysis.
  arXiv  Detail & Related papers  (2024-10-09T15:02:28Z)
• MaskUno: Switch-Split Block For Enhancing Instance Segmentation [0.0]
  We propose replacing mask prediction with a Switch-Split block that processes refined ROIs, classifies them, and assigns them to specialized mask predictors. An increase in the mean Average Precision (mAP) of 2.03% was observed for the high-performing DetectoRS when trained on 80 classes.
  arXiv  Detail & Related papers  (2024-07-31T10:12:14Z)
• DynaMask: Dynamic Mask Selection for Instance Segmentation [21.50329070835023]
  We develop a Mask Switch Module (MSM) with negligible computational cost to select the most suitable mask resolution for each instance. The proposed method, namely DynaMask, brings consistent and noticeable performance improvements over other state-of-the-arts at a moderate computation overhead.
  arXiv  Detail & Related papers  (2023-03-14T13:01:25Z)
• Mask3D: Mask Transformer for 3D Semantic Instance Segmentation [89.41640045953378]
  We show that we can leverage generic Transformer building blocks to directly predict instance masks from 3D point clouds. Using Transformer decoders, the instance queries are learned by iteratively attending to point cloud features at multiple scales. Mask3D sets a new state-of-the-art on ScanNet test (+6.2 mAP), S3DIS 6-fold (+10.1 mAP),LS3D (+11.2 mAP) and ScanNet200 test (+12.4 mAP)
  arXiv  Detail & Related papers  (2022-10-06T17:55:09Z)
• Instance and Panoptic Segmentation Using Conditional Convolutions [96.7275593916409]
  We propose a simple yet effective framework for instance and panoptic segmentation, termed CondInst. We show that CondInst can achieve improved accuracy and inference speed on both instance and panoptic segmentation tasks.
  arXiv  Detail & Related papers  (2021-02-05T06:57:02Z)
• Mask Encoding for Single Shot Instance Segmentation [97.99956029224622]
  We propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst) Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance.
  arXiv  Detail & Related papers  (2020-03-26T02:51:17Z)
• SOLOv2: Dynamic and Fast Instance Segmentation [102.15325936477362]
  We build a simple, direct, and fast instance segmentation framework with strong performance. We take one step further by dynamically learning the mask head of the object segmenter. We demonstrate a simple direct instance segmentation system, outperforming a few state-of-the-art methods in both speed and accuracy.
  arXiv  Detail & Related papers  (2020-03-23T09:44:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.