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Construction of a universal detector poses a crucial question: How can wemost effectively train a model on a large mixture of datasets? The answer liesin learning dataset-specific features and ensembling their knowledge but do allthis in a single model. Previous methods achieve this by having separatedetection heads on a common backbone but that results in a significant increasein parameters. In this work, we present Mixture-of-Experts as a solution,highlighting that MoEs are much more than a scalability tool. We proposeDataset-Aware Mixture-of-Experts, DAMEX where we train the experts to become an`expert' of a dataset by learning to route each dataset tokens to its mappedexpert. Experiments on Universal Object-Detection Benchmark show that weoutperform the existing state-of-the-art by average +10.2 AP score and improveover our non-MoE baseline by average +2.0 AP score. We also observe consistentgains while mixing datasets with (1) limited availability, (2) disparatedomains and (3) divergent label sets. Further, we qualitatively show that DAMEXis robust against expert representation collapse.

DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets