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DeepLabv3

53 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

DeepLabv3 is a semantic segmentation architecture that improves upon DeepLabv2 with several modifications. To handle the problem of segmenting objects at multiple scales, modules are designed which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, the Atrous Spatial Pyramid Pooling module from DeepLabv2 augmented with image-level features encoding global context and further boost performance.

The changes to the ASSP module are that the authors apply global average pooling on the last feature map of the model, feed the resulting image-level features to a 1 × 1 convolution with 256 filters (and batch normalization), and then bilinearly upsample the feature to the desired spatial dimension. In the end, the improved ASPP consists of (a) one 1×1 convolution and three 3 × 3 convolutions with rates = (6, 12, 18) when output stride = 16 (all with 256 filters and batch normalization), and (b) the image-level features.

Another interesting difference is that DenseCRF post-processing from DeepLabv2 is no longer needed.

Source: Rethinking Atrous Convolution for Semantic Image SegmentationSee Code · tensorflow/models

Papers archive 2025-07-28

30 shown of 53, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 97 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Semantic Segmentation40
Segmentation25
Image Segmentation14
Object Detection11
object-detection8
Image Classification7
Instance Segmentation7
image-classification5
Classification3
Decoder3
Neural Architecture Search3
Object3
Anatomy2
Anomaly Detection2
Autonomous Driving2
Depth Estimation2
Domain Adaptation2
Image Augmentation2
Knowledge Distillation2
Medical Image Analysis2

Usage over time archive 2025-07-28

Papers per year tagged with DeepLabv3: 2017 to 2025, peak 11 11 0 2017: 1 paper 2017 2018: 3 papers 2018 2019: 5 papers 2019 2020: 7 papers 2020 2021: 11 papers 2021 2022: 8 papers 2022 2023: 5 papers 2023 2024: 9 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (53 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Semantic Segmentation Models

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