ICCV 2025 Papers with Code & Data

2025-10-20 · Source: Computer Vision – Resources | Paper Digest · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Natural Language Processing · Depth: Expert, extended

Summary

This compilation provides an extensive index of accepted papers with associated public code or data repositories from the International Conference on Computer Vision (ICCV) 2025, scheduled to begin on October 19th in Hawaii. The index was generated through an automated process, with efforts made for completeness. Readers are advised that some code repositories may not be fully public until the conference officially starts. The Paper Digest Team curated this list and encourages exploration of related resources, including curated summaries of ICCV 2025 papers and a historical overview of influential ICCV papers since 1988. The platform also offers AI-powered tools for personalized daily paper digests, article reading, writing, Q&A, literature reviews, and research report generation.

Key takeaway

For research scientists and practitioners, this ICCV 2025 index offers immediate access to a wealth of open-source implementations and datasets, significantly reducing the barrier to replicating and building upon cutting-edge computer vision research. You should consult this resource early to identify relevant projects and leverage the provided code and data to accelerate your own development and experimentation, especially as repositories become fully public closer to the conference date.

Key insights

ICCV 2025 papers with code and data are indexed to foster community engagement and accelerate research.

Principles

• Open access to code and data accelerates research.
• Automated indexing facilitates rapid community engagement.

Method

An automated extraction process compiles public code and data repositories associated with accepted conference papers, creating a searchable index.

In practice

• Access public code and data for ICCV 2025 papers.
• Utilize Paper Digest's AI tools for research tasks.

Topics

• Diffusion Models
• Vision-Language Models
• 3D Reconstruction
• Autonomous Driving
• Medical Imaging

Code references

• facebookresearch/webssl
• embeddings-benchmark/mteb
• THU-MIG/yoloe
• bytedance/1d-tokenizer
• zyb5/LUT-Fuse

Best for: Research Scientist, AI Researcher, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision – Resources | Paper Digest.