This repository is a curated list of resources for object detection, specifically focusing on object proposal generation techniques. It serves researchers and practitioners in computer vision who need to understand and implement methods for efficiently identifying potential object locations within images. The collection aims to provide a comprehensive overview of seminal papers, code, datasets, and evaluation benchmarks in this domain.

How It Works

The project is a curated list, not a software library. It organizes resources into categories such as Objectness Scoring, Similarity Grouping, Supervised Learning, Hybrid & Part-based methods, Spatio-Temporal approaches, Low-Level Processing, Evaluation, Datasets, and Object Detection frameworks. This structure allows users to navigate the landscape of object proposal research, from foundational concepts to state-of-the-art deep learning models.

Highlighted Details

• Comprehensive coverage of object proposal methods, including foundational techniques like Selective Search and EdgeBoxes, as well as deep learning-based approaches like RPN and DeepMask.
• Links to seminal papers, associated code repositories (where available), and relevant datasets (PASCAL, COCO, ImageNet, KITTI).
• Includes resources for evaluating object proposal performance, such as the Hosang benchmark.
• Features sections on related topics like objectness scoring, similarity grouping, and low-level image processing techniques.

Maintenance & Community

This is a community-curated list. The author encourages contributions via pull requests to expand and update the resources.

Licensing & Compatibility

The repository itself is a list of links and does not have a specific software license. The licenses of the linked papers, code, and datasets vary and must be checked individually.

Limitations & Caveats

This is a reference list and does not provide executable code or a unified framework. Users must independently find, install, and integrate the various tools and datasets mentioned. The rapid evolution of object detection means some listed methods may be superseded by newer techniques.