Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

• URL: http://arxiv.org/abs/2012.07177v1
• Date: Sun, 13 Dec 2020 22:59:45 GMT
• Title: Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
• Authors: Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph
• Abstract summary: We study the Copy-Paste augmentation ([13, 12]) for instance segmentation where we randomly paste objects onto an image. We find that the simple mechanism of pasting objects randomly is good enough and can provide solid gains on top of strong baselines. Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3.6 mask AP on rare categories.
• Score: 94.4931516162023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation ([13, 12]) for instance segmentation where we randomly paste objects onto an image. Prior studies on Copy-Paste relied on modeling the surrounding visual context for pasting the objects. However, we find that the simple mechanism of pasting objects randomly is good enough and can provide solid gains on top of strong baselines. Furthermore, we show Copy-Paste is additive with semi-supervised methods that leverage extra data through pseudo labeling (e.g. self-training). On COCO instance segmentation, we achieve 49.1 mask AP and 57.3 box AP, an improvement of +0.6 mask AP and +1.5 box AP over the previous state-of-the-art. We further demonstrate that Copy-Paste can lead to significant improvements on the LVIS benchmark. Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3.6 mask AP on rare categories.

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)
• Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation [8.335108002480068]
  MMSports 2023 DeepSportRadar has introduced a dataset that focuses on segmenting human subjects within a basketball context. This challenge demands the application of robust data augmentation techniques and wisely-chosen deep learning architectures. Our work (ranked 1st in the competition) first proposes a novel data augmentation technique, capable of generating more training samples with wider distribution.
  arXiv  Detail & Related papers  (2023-10-27T07:44:25Z)
• X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion [137.84635386962395]
  Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. We revisit Copy-Paste at scale with the power of newly emerged zero-shot recognition models. X-Paste provides impressive improvements over the strong baseline CenterNet2 with Swin-L as the backbone.
  arXiv  Detail & Related papers  (2022-12-07T18:59:59Z)
• BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training [44.32782190757813]
  We construct a new large-scale benchmark termed BigDetection. Our dataset has 600 object categories and contains over 3.4M training images with 36M bounding boxes.
  arXiv  Detail & Related papers  (2022-03-24T17:57:29Z)
• Instance Segmentation Challenge Track Technical Report, VIPriors Workshop at ICCV 2021: Task-Specific Copy-Paste Data Augmentation Method for Instance Segmentation [0.0]
  Copy-Paste has proven to be a very effective data augmentation for instance segmentation. We applied additional data augmentation techniques including RandAugment and GridMask. We reached 0.477 AP@0.50:0.95 with the test set by adding the validation set to the training data.
  arXiv  Detail & Related papers  (2021-10-01T15:03:53Z)
• Semi-Supervised Domain Adaptation with Prototypical Alignment and Consistency Learning [86.6929930921905]
  This paper studies how much it can help address domain shifts if we further have a few target samples labeled. To explore the full potential of landmarks, we incorporate a prototypical alignment (PA) module which calculates a target prototype for each class from the landmarks. Specifically, we severely perturb the labeled images, making PA non-trivial to achieve and thus promoting model generalizability.
  arXiv  Detail & Related papers  (2021-04-19T08:46:08Z)
• The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation [93.17367076148348]
  We investigate performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset. We unveil that a major cause is the inaccurate classification of object proposals. We propose a simple calibration framework to more effectively alleviate classification head bias with a bi-level class balanced sampling approach.
  arXiv  Detail & Related papers  (2020-07-23T12:49:07Z)
• EOLO: Embedded Object Segmentation only Look Once [0.0]
  We introduce an anchor-free and single-shot instance segmentation method, which is conceptually simple with 3 independent branches, fully convolutional and can be used by easily embedding it into mobile and embedded devices. Our method, refer as EOLO, reformulates the instance segmentation problem as predicting semantic segmentation and distinguishing overlapping objects problem, through instance center classification and 4D distance regression on each pixel. Without any bells and whistles, EOLO achieves 27.7$%$ in mask mAP under IoU50 and reaches 30 FPS on 1080Ti GPU, with a single-model and single-scale training/testing on
  arXiv  Detail & Related papers  (2020-03-31T21:22:05Z)
• 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)

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.