A multi-aspect knowledge distillation method that appends binary question-answer logits from an MLLM to a classifier's output improves fine-grained image classification accuracy by up to about 6 points.
Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories
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Multi-aspect Knowledge Distillation with Large Language Model
A multi-aspect knowledge distillation method that appends binary question-answer logits from an MLLM to a classifier's output improves fine-grained image classification accuracy by up to about 6 points.