MDETR - Modulated Detection for End-to-End Multi-Modal Understanding

Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. Our approach can be easily extended for visual question answering, achieving competitive performance on GQA and CLEVR.

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• 1 However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes.
• 2 This makes it challenging for such systems to capture the long tail of visual concepts expressed in free form text.
• 3 In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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