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Strip Pooling Network

SPNet

5 papers tagged archive 2025-07-28

Introduced by Qibin Hou et al. in Strip Pooling: Rethinking Spatial Pooling for Scene Parsing

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

Spatial pooling usually operates on a small region which limits its capability to capture long-range dependencies and focus on distant regions. To overcome this, Hou et al. proposed strip pooling, a novel pooling method capable of encoding long-range context in either horizontal or vertical spatial domains.

Strip pooling has two branches for horizontal and vertical strip pooling. The horizontal strip pooling part first pools the input feature F ∈ℛ^(C ×H ×W) in the horizontal direction: y¹ = GAPʷ (X) Then a 1D convolution with kernel size 3 is applied in y to capture the relationship between different rows and channels. This is repeated W times to make the output yᵥ consistent with the input shape: yₕ = Expand(Conv1D(y¹)) Vertical strip pooling is performed in a similar way. Finally, the outputs of the two branches are fused using element-wise summation to produce the attention map: s = σ(Conv^(1×1)(yᵥ + yₕ)) Y = s X

The strip pooling module (SPM) is further developed in the mixed pooling module (MPM). Both consider spatial and channel relationships to overcome the locality of convolutional neural networks. SPNet achieves state-of-the-art results for several complex semantic segmentation benchmarks.

PaperSource

Papers archive 2025-07-28

5 shown of 5, 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

10 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 Segmentation2
3D Object Detection1
Autonomous Driving1
Decoder1
Object Detection1
Person Search1
Retinal Vessel Segmentation1
Scene Parsing1
Segmentation1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with SPNet: 2020 to 2024, peak 2 2 0 2020: 1 paper 2020 2021: 1 paper 2021 2022: 2 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (5 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

Attention Mechanisms

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