Methods › Computer Vision › Vision Transformers › CCT

Compact Convolutional Transformers

CCT

33 papers tagged archive 2025-07-28

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

Compact Convolutional Transformers utilize sequence pooling and replace the patch embedding with a convolutional embedding, allowing for better inductive bias and making positional embeddings optional. CCT achieves better accuracy than ViT-Lite (smaller ViTs) and increases the flexibility of the input parameters.

Source: Escaping the Big Data Paradigm with Compact Transformers

Papers archive 2025-07-28

30 shown of 33, 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 56 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
Image Classification6
image-classification4
Classification2
Decision Making2
Decoder2
Facial Expression Recognition2
Facial Expression Recognition (FER)2
Memorization2
Position2
Retrieval2
Segmentation2
Semantic Segmentation2
Text Generation2
Transfer Learning2
Autonomous Driving1
Binary Classification1
Bug fixing1
Chunking1
Clone Detection1
Code Search1

Usage over time archive 2025-07-28

Papers per year tagged with CCT: 2021 to 2025, peak 8 8 0 2021: 6 papers 2021 2022: 8 papers 2022 2023: 7 papers 2023 2024: 8 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (33 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

Vision Transformers

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