# amusi/daily-paper-computer-vision

记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文

Repository: https://github.com/amusi/daily-paper-computer-vision
Canonical: https://ross.abutalabs.com/products/daily-paper-computer-vision
License Family: other
Topics: paper, deep-learning, computer-vision, machine-learning, face-detection, object-detection
Last push: 2023-07-08T08:14:09+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": 3032, "days_push": 1152, "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 6780, forks 1268 (observed 2026-08-28T04:09:48.556586+00:00)

## What it is
A curated daily digest of computer vision, deep learning, and machine learning research papers, with links to top-conference paper lists (CVPR, ECCV, NeurIPS, ICLR, etc.) from 2017-2023. It serves as a reference index for tracking new CV/AI papers rather than a software tool.

## Use cases
- find latest computer vision papers daily
- track CVPR and ECCV accepted papers with code
- stay updated on object detection and segmentation research
- browse top AI conference paper lists by year
- discover new deep learning research to read

## When to choose
- you want a curated, human-organized list of CV/AI papers and conference links
- you need a quick reference for top-conference papers from 2017-2023

## When to avoid
- you need automated paper search or recommendation tooling
- you want runnable code or a library rather than a paper index

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, search-engine
- domain: computer-vision, deep-learning, machine-learning, tutorials
- platform: cross-platform
- tags: paper-digest, cvpr, arxiv-daily, research-papers, awesome-list

## Member repositories
- amusi/daily-paper-computer-vision (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:48.556586+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 2026-08-29T17:42:22.351808+00:00, confidence not recorded.
  - readme: https://github.com/amusi/daily-paper-computer-vision (fetched 2026-08-28T04:09:48.556586+00:00, sha 7c57c277df8b)
- Data as of 2026-08-30T08:39:29.467469+00:00.