# cmhungsteve/Awesome-Transformer-Attention

An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

Repository: https://github.com/cmhungsteve/Awesome-Transformer-Attention
Canonical: https://ross.abutalabs.com/products/awesome-transformer-attention
License Family: other
Topics: transformer, attention-mechanism, vision-transformer, deep-learning, awesome-list, transformer-cv, transformer-architecture, transformer-awesome, transformer-with-cv, transformer-models, visual-transformer, computer-vision, papers, attention-mechanisms, self-attention, vit, detr, transformers
Last push: 2024-07-30T06:57:18+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1813, "days_push": 764, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5049, forks 498 (observed 2026-08-28T04:09:08.947688+00:00)

## What it is
A curated awesome-list of research papers, code, and resources on Vision Transformers and attention mechanisms for computer vision. It is actively updated with papers from major conferences like CVPR, ICCV, NeurIPS, ICML, and ICLR.

## Use cases
- find papers on vision transformers
- survey attention mechanisms in computer vision
- research ViT architectures for image classification
- find code implementations of transformer papers
- keep up with latest transformer research from CVPR and NeurIPS
- learn about efficient vision transformer designs

## When to choose
- you need a comprehensive, categorized reading list on vision transformers and attention
- you want links to paper code implementations
- you are doing a literature review on transformer-based computer vision

## When to avoid
- you need runnable software or a library rather than a paper list
- you need tutorials or courses rather than research paper references

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: deep-learning, computer-vision, artificial-intelligence, awesome-lists
- platform: cross-platform
- tags: awesome-list, vision-transformer, attention, papers, vit, self-attention, research-papers

## Member repositories
- cmhungsteve/Awesome-Transformer-Attention (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:08.947688+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:17:21.776566+00:00, confidence not recorded.
  - readme: https://github.com/cmhungsteve/Awesome-Transformer-Attention (fetched 2026-08-28T04:09:08.947688+00:00, sha e22641322c8b)
- Data as of 2026-08-30T08:39:29.467469+00:00.