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.
Chain-of-thought prompting elicits reasoning in large lan- guage models.Advances in neural information processing systems, 35:24824–24837, 2022
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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.