FruitEnsemble uses a weighted ensemble of backbones for top-3 candidates followed by MLLM arbitration on low-confidence samples to reach 70.49% accuracy on a new 306-class fruit dataset.
Classification with a reject option using a hinge loss.Journal of Machine Learn- ing Research, 9(8)
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FruitEnsemble: MLLM-Guided Arbitration for Heterogeneous ensemble in Fine-Grained Fruit Recognition
FruitEnsemble uses a weighted ensemble of backbones for top-3 candidates followed by MLLM arbitration on low-confidence samples to reach 70.49% accuracy on a new 306-class fruit dataset.