Methods › General › Affinity Functions › Concatenation Affinity

Concatenation Affinity

1 paper tagged archive 2025-07-28

Introduced by Xiaolong Wang et al. in Non-local Neural Networks

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

Concatenation Affinity is a type of affinity or self-similarity function between two points 𝕩ᵢ and 𝕩ⱼ that uses a concatenation function:

f(𝕩ᵢ, 𝕩ⱼ) = ReLU(𝕨ᵀ_f[θ(𝕩ᵢ), ϕ(𝕩ⱼ)])

Here [·, ·] denotes concatenation and 𝕨_f is a weight vector that projects the concatenated vector to a scalar.

PaperSourceSee Code · tea1528/Non-Local-NN-Pytorch

Papers archive 2025-07-28

1 shown of 1, 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

9 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
Action Classification1
Action Recognition1
Instance Segmentation1
Keypoint Detection1
Object Detection1
Pose Estimation1
Position1
Text-To-SQL1
Video Classification1

Usage over time archive 2025-07-28

Papers per year tagged with Concatenation Affinity: 2017 to 2017, peak 1 1 0 2017: 1 paper 2017
Papers per year the archive tags with this method, by the paper's archive date (1 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

Affinity Functions

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