Object detection with transformer on many domains by attention to past predictions with Cascade-DETR

Object detection with transformer on many domains by attention to past predictions with Cascade-DETR

Cascade-DETR: Delving into High-Quality Universal Object Detection

arXiv paper abstract https://arxiv.org/abs/2307.11035

arXiv PDF paper https://arxiv.org/pdf/2307.11035.pdf

... While dominating on the COCO benchmark, recent Transformer-based detection methods are not competitive in diverse domains.

Moreover, these methods still struggle to very accurately estimate the object bounding boxes in complex environments.

... introduce Cascade-DETR for high-quality universal object detection.

... jointly tackle the generalization to diverse domains and localization accuracy by proposing the Cascade Attention layer, which explicitly integrates object-centric information into the detection decoder by limiting the attention to the previous box prediction.

... Instead of relying on classification scores, ... predict the expected IoU of the query, leading to substantially more well-calibrated confidences.

... While also advancing the state-of-the-art on COCO, Cascade-DETR substantially improves DETR-based detectors on all datasets in UDB10, even by over 10 mAP in some cases ...

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