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Despite achieving impressive accuracy, the model's performance falters significantly on unseen data, raising concerns about its reliability in real-world applications.
Robustness in Open Vocabulary Object Detectors is driven more by image domain characteristics than by annotation methods, challenging existing assumptions about model training.
Gradient-trained models struggle with unseen bacterial combinations, but lightweight anchor-based decoders can significantly improve identification accuracy in open-world scenarios.
