# amusi/CVPR2026-Papers-with-Code

CVPR 2026 论文和开源项目合集

Repository: https://github.com/amusi/CVPR2026-Papers-with-Code
Canonical: https://ross.abutalabs.com/products/cvpr2026-papers-with-code
License Family: other
Topics: cvpr, cvpr2020, computer-vision, deep-learning, machine-learning, object-detection, image-segmentation, paper, image-processing, visual-tracking, python, cvpr2021, semantic-segmentation, cvpr2022, transformer, transformers, cvpr2023, cvpr2024, cvpr2025, cvpr2026
Last push: 2026-03-08T07:27:34+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 71, release rhythm 35, longevity 100
- inputs: {"age_days": 2380, "days_push": 178, "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 22796, forks 2797 (observed 2026-08-28T04:11:34.080738+00:00)

## What it is
A curated collection of CVPR 2026 accepted papers and their associated open-source code repositories, organized by computer vision research topics. It serves as a reference catalog for tracking state-of-the-art research across areas like object detection, segmentation, 3D vision, and generative models.

## Use cases
- find CVPR 2026 papers with code implementations
- track state-of-the-art computer vision research
- discover open-source projects for object detection and segmentation
- research diffusion models and vision transformers
- find papers on 3D reconstruction and Gaussian splatting
- survey latest medical image segmentation methods
- keep up with autonomous driving vision research

## When to choose
- you want a topic-organized index of CVPR 2026 papers with code links
- you're surveying the state of the art in computer vision
- you need reference implementations for a specific CV subfield

## When to avoid
- you need a runnable software tool rather than a paper catalog
- you're looking for papers from other conferences like ICCV or ECCV
- you need peer-reviewed summaries or tutorials rather than paper links

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, search-engine
- domain: computer-vision, deep-learning, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: cvpr, papers-with-code, curated-list, research-papers, computer-vision, open-source-projects, conference-papers

## Member repositories
- amusi/CVPR2026-Papers-with-Code (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:34.080738+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 2026-08-29T16:56:55.407628+00:00, confidence not recorded.
  - readme: https://github.com/amusi/CVPR2026-Papers-with-Code (fetched 2026-08-28T04:11:34.080738+00:00, sha 727afd0ba5f4)
- Data as of 2026-08-30T08:39:29.467469+00:00.