# MenghaoGuo/Awesome-Vision-Attentions

Summary of related papers on visual attention. Related code will be released based on Jittor gradually.

Repository: https://github.com/MenghaoGuo/Awesome-Vision-Attentions
Canonical: https://ross.abutalabs.com/products/awesome-vision-attentions
Language: Python
License Family: other
Last push: 2024-10-20T07:49:48+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": 1827, "days_push": 682, "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 2842, forks 400 (observed 2026-08-28T04:07:24.830535+00:00)

## What it is
A curated awesome-list of papers on visual attention mechanisms in computer vision, accompanying the survey 'Attention Mechanisms in Computer Vision: A Survey'. It organizes papers by attention type (channel, spatial, temporal, branch) and provides reference implementations in Jittor.

## Use cases
- find papers on attention mechanisms in computer vision
- learn about channel and spatial attention for CNNs
- survey visual attention methods for a literature review
- get Jittor implementations of attention modules like SE and ECANet
- research attention mechanisms for image classification or segmentation
- find highly-cited attention papers for deep learning models

## When to choose
- you need a categorized reading list of vision attention papers
- you want reference Jittor code for attention modules
- you are writing a survey or literature review on attention in computer vision

## When to avoid
- you need production-ready PyTorch or TensorFlow attention implementations
- you want a maintained library with releases and support
- you need non-vision attention methods like NLP transformers

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, documentation
- domain: computer-vision, deep-learning, artificial-intelligence, tutorials
- platform: python
- tags: awesome-list, attention-mechanisms, paper-collection, survey, jittor

## Member repositories
- MenghaoGuo/Awesome-Vision-Attentions (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:24.830535+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 2026-08-30T07:37:13.528686+00:00, confidence not recorded.
  - readme: https://github.com/MenghaoGuo/Awesome-Vision-Attentions (fetched 2026-08-28T04:07:24.830535+00:00, sha 2116bae31425)
- Data as of 2026-08-30T08:39:29.467469+00:00.