RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features

• URL: http://arxiv.org/abs/2104.08569v1
• Date: Sat, 17 Apr 2021 15:09:20 GMT
• Title: RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
• Authors: Gang Zhang, Xin Lu, Jingru Tan, Jianmin Li, Zhaoxiang Zhang, Quanquan Li, Xiaolin Hu
• Abstract summary: RefineMask is a new method for high-quality instance segmentation of objects and scenes. It incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner. It succeeds in segmenting hard cases such as bent parts of objects that are over-smoothed by most previous methods.
• Score: 53.71163467683838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsampling operations in both the feature pyramid and the instance-wise pooling process, especially for large objects. In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner. Through fusing more detailed information stage by stage, RefineMask is able to refine high-quality masks consistently. RefineMask succeeds in segmenting hard cases such as bent parts of objects that are over-smoothed by most previous methods and outputs accurate boundaries. Without bells and whistles, RefineMask yields significant gains of 2.6, 3.4, 3.8 AP over Mask R-CNN on COCO, LVIS, and Cityscapes benchmarks respectively at a small amount of additional computational cost. Furthermore, our single-model result outperforms the winner of the LVIS Challenge 2020 by 1.3 points on the LVIS test-dev set and establishes a new state-of-the-art. Code will be available at https://github.com/zhanggang001/RefineMask.

Related papers

• 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)
• Mask Transfiner for High-Quality Instance Segmentation [95.74244714914052]
  We present Mask Transfiner for high-quality and efficient instance segmentation. Our approach only processes detected error-prone tree nodes and self-corrects their errors in parallel. Our code and trained models will be available at http://vis.xyz/pub/transfiner.
  arXiv  Detail & Related papers  (2021-11-26T18:58:22Z)
• 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)
• PointINS: Point-based Instance Segmentation [117.38579097923052]
  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.
  arXiv  Detail & Related papers  (2020-03-13T08:24:58Z)
• BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation [103.74690082121079]
  In this work, we achieve improved mask prediction by effectively combining instance-level information with semantic information with lower-level fine-granularity. Our main contribution is a blender module which draws inspiration from both top-down and bottom-up instance segmentation approaches. BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer.
  arXiv  Detail & Related papers  (2020-01-02T03:30:17Z)

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.