mlsanityData to 7 Oct 2026

DeepLab2: A TensorFlow Library for Deep Labeling

arXiv 2106.09748 ↗First posted 17 Jun 2021cs.CVcited by 22

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Abstract (arXiv)

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision. DeepLab2 includes all our recently developed DeepLab model variants with pretrained checkpoints as well as model training and evaluation code, allowing the community to reproduce and further improve upon the state-of-art systems. To showcase the effectiveness of DeepLab2, our Panoptic-DeepLab employing Axial-SWideRNet as network backbone achieves 68.0% PQ or 83.5% mIoU on Cityscaspes validation set, with only single-scale inference and ImageNet-1K pretrained checkpoints. We hope that publicly sharing our library could facilitate future research on dense pixel labeling tasks and envision new applications of this technology. Code is made publicly available at urlhttps://github.com/google-research/deeplab2.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 59 of its 76 references that are in the map. First posted on arXiv 17 Jun 2021.

Builds on

59 of 76 references.

References · 59

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38 more
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 10; 2024: 2; 2025: 2; 2026: 1.

22 papers.

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  1. 2022Towards End-to-End Unified Scene Text Detection and Layout Analysiscited by 96
  2. 2022CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentationcited by 60
  3. 2022Waymo Open Dataset: Panoramic Video Panoptic Segmentationcited by 57
  4. 2022MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Modelscited by 47
  5. 2022TubeFormer-DeepLab: Video Mask Transformercited by 35
  6. 2023Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentationcited by 24
  7. 2024COCONut: Modernizing COCO Segmentationcited by 24
  8. 2023ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentationcited by 18
  9. 2023PolyMaX: General Dense Prediction with Mask Transformercited by 14
  10. 2023EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentationcited by 13
  11. 2022kMaX-DeepLab: k-means Mask Transformercited by 12
  12. 2023Superpixel Transformers for Efficient Semantic Segmentationcited by 12
10 more
  1. 2023A Study of Autoregressive Decoders for Multi-Tasking in Computer Visioncited by 10
  2. 2023SANPO: A Scene Understanding, Accessibility and Human Navigation Datasetcited by 10
  3. 2022Artemis: Articulated Neural Pets with Appearance and Motion synthesiscited by 8
  4. 2023ClusterFuG: Clustering Fully connected Graphs by Multicutcited by 5
  5. 2023MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentationcited by 4
  6. 2023A Simple Video Segmenter by Tracking Objects Along Axial Trajectoriescited by 3
  7. 2024Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statisticscited by 1
  8. 2025SAGE: Semantic-Driven Adaptive Gaussian Splatting in Extended Realitycited by 0
  9. 2025Weakly-supervised Localization of Manipulated Image Regions Using Multi-resolution Learned Featurescited by 0
  10. 2026Know-Your-Scene (KYS)-SLAM: Hierarchical Semantic-Motion Priors for Feature Matching in Stereo Visual SLAMcited by 0