# amusi/ICCV2025-Papers-with-Code

ICCV 2025 论文和开源项目合集

Repository: https://github.com/amusi/ICCV2025-Papers-with-Code
Canonical: https://ross.abutalabs.com/products/iccv2025-papers-with-code
License Family: other
Topics: iccv, iccv2021, object-detection, computer-vision, artificial-intelligence, semantic-segmentation, transformer, iccv2023, iccv2025
Last push: 2025-07-06T12:19:14+00:00

## Health v2 (maintenance only)
Score: 46/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 30, release rhythm 35, longevity 100
- inputs: {"age_days": 1868, "days_push": 423, "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 2884, forks 255 (observed 2026-08-28T04:07:28.155356+00:00)

## What it is
A curated collection of ICCV 2025 accepted papers (2699 papers, 24% acceptance rate) along with their associated open-source code repositories. It organizes papers by computer vision research topics such as object detection, semantic segmentation, diffusion models, 3D reconstruction, and multimodal LLMs.

## Use cases
- find ICCV 2025 papers with code for object detection
- browse latest computer vision research by topic
- track open-source implementations of CV conference papers
- find state-of-the-art semantic segmentation papers
- discover diffusion model and image generation research
- find papers on 3D reconstruction and Gaussian splatting
- research medical image segmentation methods
- survey recent vision-language and multimodal LLM work

## When to choose
- you want a topic-organized index of ICCV 2025 papers with code links
- you are surveying the state of the art in computer vision
- you want to follow top CV conference output across many subfields

## When to avoid
- you need the papers themselves rather than links and summaries
- you need papers from other conferences like CVPR or ECCV
- you need a searchable database with metadata rather than a curated list

## Facets
- artifact type: dataset
- maturity: active
- function: computer-vision, deep-learning, machine-learning
- domain: computer-vision, artificial-intelligence, deep-learning, image-processing, autonomous-vehicles, healthcare
- platform: cross-platform
- tags: awesome-list, papers-with-code, iccv2025, curated-list, research-papers, open-source-projects

## Member repositories
- amusi/ICCV2025-Papers-with-Code (main) score 46

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:28.155356+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 2026-08-29T18:48:18.828084+00:00, confidence not recorded.
  - readme: https://github.com/amusi/ICCV2025-Papers-with-Code (fetched 2026-08-28T04:07:28.155356+00:00, sha fa5a6ef4c29b)
- Data as of 2026-08-30T08:39:29.467469+00:00.