ProCal: Proposal Calibration in open-vocabulary detection

Object detection in open-vocabulary environments represents one of the most fascinating challenges in the field of computer vision: a system must locate and classify objects it has never seen during training. Modern models often rely on pre-trained visual language models (VLMs), fixing their weights as the detector's backbone. However, these models have a critical limitation: the VLM's classification score lacks an explicit understanding of the object's position and scale within the image. This is where solutions like ProCal (Proposal Calibration) emerge, an inference-time calibration approach that improves the localization quality of classification scores. ProCal combines two signals: a location-aware foreground score, which determines whether a proposal contains an object area, and a background-aware suppression score, which measures how much the proposal resembles the background. This dual correction allows suppressing false activations on background proposals and correctly positioning novel object proposals. Results on CLIPSelf ViT-L/14 show a significant improvement in AP (Average Precision) for unseen categories. Behind these advances in artificial intelligence applied to vision, a solid technological foundation is needed to integrate these models into production environments. At Q2BSTUDIO we develop custom applications that incorporate these cutting-edge algorithms, ensuring scalability and performance. Additionally, we offer artificial intelligence services for businesses that range from implementing open-vocabulary detection models to creating custom AI agents. Proper proposal calibration, like ProCal, directly benefits from robust platforms on AWS and Azure cloud services, where low-latency inference pipelines can be deployed. Cybersecurity also plays a key role in protecting training data and predictions, especially in sensitive sectors. Finally, integration with business intelligence tools like Power BI allows real-time visualization of detection results, facilitating data-driven decision-making. ProCal is an example of how research in model calibration can enhance practical applications when combined with custom software development and optimized cloud infrastructure.