DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection

• URL: http://arxiv.org/abs/2503.09271v1
• Date: Wed, 12 Mar 2025 11:15:34 GMT
• Title: DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection
• Authors: Chiara Cappellino, Gianluca Mancusi, Matteo Mosconi, Angelo Porrello, Simone Calderara, Rita Cucchiara,
• Abstract summary: DitHub is a framework designed to create and manage a library of efficient adaptation modules.<n>Inspired by Version Control Systems, DitHub organizes expert modules like branches that can be fetched and merged as needed.<n>Our approach achieves state-of-the-art performance on the ODinW-13 benchmark and ODinW-O, a newly introduced benchmark.
• Score: 32.77455136447568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-Vocabulary object detectors can recognize a wide range of categories using simple textual prompts. However, improving their ability to detect rare classes or specialize in certain domains remains a challenge. While most recent methods rely on a single set of model weights for adaptation, we take a different approach by using modular deep learning. We introduce DitHub, a framework designed to create and manage a library of efficient adaptation modules. Inspired by Version Control Systems, DitHub organizes expert modules like branches that can be fetched and merged as needed. This modular approach enables a detailed study of how adaptation modules combine, making it the first method to explore this aspect in Object Detection. Our approach achieves state-of-the-art performance on the ODinW-13 benchmark and ODinW-O, a newly introduced benchmark designed to evaluate how well models adapt when previously seen classes reappear. For more details, visit our project page: https://aimagelab.github.io/DitHub/

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