# willard-yuan/awesome-cbir-papers

📝Awesome and classical image retrieval papers

Repository: https://github.com/willard-yuan/awesome-cbir-papers
Canonical: https://ross.abutalabs.com/products/awesome-cbir-papers
License Family: other
Topics: cbir, image-retrieval, visual-search, image-retrieval-papers, nearest-neighbor-search, instance-retrieval, local-features
Last push: 2026-08-25T10:40:38+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 4023, "days_push": 8, "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 1758, forks 287 (observed 2026-08-28T04:05:32.388152+00:00)

## What it is
A curated awesome-list of classical and deep learning papers on content-based image retrieval (CBIR), covering local features, global features, instance search, ANN search, and industry systems. It also links to tutorials, datasets, demos, and useful packages for image retrieval research.

## Use cases
- find papers on content-based image retrieval
- research deep learning features for image search
- learn about SIFT and classical local feature retrieval methods
- find datasets for image retrieval benchmarks
- survey approximate nearest neighbor search methods
- study industrial image retrieval systems
- find tutorials on visual search and CBIR

## When to choose
- you are researching or surveying image retrieval literature
- you need a starting point for CBIR, instance search, or ANN search papers
- you want curated links to datasets and tutorials for visual search

## When to avoid
- you need runnable image retrieval software rather than papers
- you want an exhaustive, automatically updated bibliography
- you need non-image retrieval topics like text or video retrieval

## Facets
- artifact type: learning-resource
- maturity: active
- function: search-engine, computer-vision, machine-learning
- domain: computer-vision, image-processing, artificial-intelligence, awesome-lists
- platform: cross-platform
- tags: cbir, image-retrieval, visual-search, paper-collection, nearest-neighbor-search, instance-retrieval, local-features, curated-list, search

## Member repositories
- willard-yuan/awesome-cbir-papers (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:32.388152+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 2026-08-30T03:27:37.987232+00:00, confidence not recorded.
  - readme: https://github.com/willard-yuan/awesome-cbir-papers (fetched 2026-08-28T04:05:32.388152+00:00, sha 34ec67b741c9)
- Data as of 2026-08-30T08:39:29.467469+00:00.