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Bottleneck Transformer Block

10 papers tagged archive 2025-07-28

Introduced by Aravind Srinivas et al. in Bottleneck Transformers for Visual Recognition

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

A Bottleneck Transformer Block is a block used in Bottleneck Transformers that replaces the spatial 3 × 3 convolution layer in a Residual Block with Multi-Head Self-Attention (MHSA).

PaperSource

Papers archive 2025-07-28

10 shown of 10, 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 23 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
Instance Segmentation2
Survival Prediction2
whole slide images2
Anatomy1
Anomaly Detection In Surveillance Videos1
Autonomous Driving1
Classification1
Data Augmentation1
Depth Estimation1
Dynamic Facial Expression Recognition1
Emotion Recognition1
Facial Expression Recognition1
General Classification1
Graph Neural Network1
Image Classification1
Object Detection1
Optical Flow Estimation1
Scene Understanding1
Segmentation1
Self-Supervised Learning1

Usage over time archive 2025-07-28

Papers per year tagged with Bottleneck Transformer Block: 2021 to 2025, peak 3 3 0 2021: 2 papers 2021 2022: 1 paper 2022 2023: 3 papers 2023 2024: 2 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (10 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 ModulesImage Model Blocks

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