This repository is a curated bibliography of research papers focusing on Mamba-based State Space Models applied to computer vision tasks. It contains no actual source code, data, or implementations - rather, it serves as a comprehensive survey organizes hundreds of recent arXiv papers by application domain including action recognition, medical imaging, remote sensing, video understanding, and many other CV subfields. The repository's sole content is a regularly updated README.md that lists papers with links to their arXiv versions and associated code repositories when available.
This repository is a curated learning roadmap for machine learning, presented as a static GitHub Pages site. It contains no executable code, notebooks, or data pipelines. The sole content is a README.md file that organizes links to courses, books, frameworks, YouTube channels, blogs, datasets, and other resources for learning ML and deep learning. The site is built using Jekyll with the slate theme, configured via _config.yml. There are no application components, APIs, or data transformations. The repository serves as a reference guide rather than a software project. The architecture is trivial: a single-page static website. The entry point is the GitHub Pages URL, which renders the README. There are no data flows, no external dependencies beyond the Jekyll theme and GitHub Pages hosting. The repository has seen community engagement (529 stars, 81 forks) but no substantive code contributions. As a software project, this repository is not reusable in the traditional sense; its value lies in the curated list of resources. There are no components suitable for extraction. The maintenance appears active based on the inclusion of recent resources, but there is no code to maintain. Technical drift is low as there are no dependencies.
This repository is a curated, annotated bibliography (awesome list) of research papers on style transfer using diffusion models. It contains no executable code, datasets, or pipelines. The value lies in taxonomizing papers into categories such as Image-Encoder Based, Swap Features in Attention Layer, Optimization-Based, LoRA-Based, Video Synthesis, 3D Generation, and Style Datasets. Each paper is listed with title, publication venue, date, and a link to its GitHub repository if available. The README also includes a brief perspective from the maintainers on the taxonomy's motivation. While the list itself is useful for researchers and practitioners in computer vision and generative models, the repository is not a functional software project and provides no reusable code or data assets.
This repository is a curated markdown list of links to free online video courses in computer science and related fields. It contains no source code, no data pipelines, no applications, and no tests. The entire substantive content resides in README.md, which organizes hundreds of video links under topical headings such as algorithms, machine learning, operating systems, and computer vision. CONTRIBUTING.md and NOTES.md provide brief guidelines explaining the philosophy of the list (concise, link-centric, no course descriptions) and contribution rules. The repository functions as a community-maintained directory or bookmark collection rather than as a software project or data research repository. While it has high star and fork counts, the code base is nonexistent; the only files of significance are three markdown documents. The last commit was in June 2026, but the list nature means that link rot is likely over time, and there is no automated validation of links.
This repository serves as a companion to the survey paper 'Towards Open Vocabulary Learning: A Survey' published in T-PAMI 2024. It contains two markdown files: README.md and dataset.md. The README provides a curated, tabular listing of papers covering open-vocabulary object detection, segmentation, video understanding, and 3D scene understanding, along with related domains. The dataset.md lists standard benchmarks and dataset splits used across these tasks. The repository does not contain any executable code, data processing pipelines, or model implementations. It is purely a literature survey resource. While useful as a reference for researchers, it has no reusable software components and no data lineage to trace.
This repository is a curated 'awesome list' of papers, datasets, and resources for talking head generation, covering image-driven, audio-driven, and NeRF/3D-based approaches. It contains no executable code, data pipelines, or models—only a single README.md with organized tables of references. The repo serves as a research bibliography, not a software project. There are no assets to reproduce or reuse programmatically.