Towards Robust Cross-Dataset Object Detection Generalization under Domain Specificity
This work addresses the challenge of robust object detection for real-world applications by providing a principled characterization of cross-dataset generalization, though it is incremental as it focuses on analysis and practical guidance rather than proposing a new method.
The paper tackles the problem of cross-dataset object detection generalization by analyzing how performance degrades when transferring between setting-agnostic and setting-specific datasets, revealing that transfer across setting types drops substantially, with the most severe breakdowns occurring from specific to agnostic targets, and open-label alignment yields consistent but bounded gains.
Object detectors often perform well in-distribution, yet degrade sharply on a different benchmark. We study cross-dataset object detection (CD-OD) through a lens of setting specificity. We group benchmarks into setting-agnostic datasets with diverse everyday scenes and setting-specific datasets tied to a narrow environment, and evaluate a standard detector family across all train--test pairs. This reveals a clear structure in CD-OD: transfer within the same setting type is relatively stable, while transfer across setting types drops substantially and is often asymmetric. The most severe breakdowns occur when transferring from specific sources to agnostic targets, and persist after open-label alignment, indicating that domain shift dominates in the hardest regimes. To disentangle domain shift from label mismatch, we compare closed-label transfer with an open-label protocol that maps predicted classes to the nearest target label using CLIP similarity. Open-label evaluation yields consistent but bounded gains, and many corrected cases correspond to semantic near-misses supported by the image evidence. Overall, we provide a principled characterization of CD-OD under setting specificity and practical guidance for evaluating detectors under distribution shift. Code will be released at \href{[https://github.com/Ritabrata04/cdod-icpr.git}{https://github.com/Ritabrata04/cdod-icpr}.