The article discusses the evaluation of Chameleon, particularly on tasks that involve generating text based on images, such as image captioning and visual question-answering. It highlights the performance comparison against prominent models like Flamingo and GPT4-V, stressing the importance of fidelity in the pre-training data. Various metrics, including CiDER scores on well-known datasets like MS-COCO, are used to assess the effectiveness of image captioning. Additionally, the article describes different fine-tuning strategies employed to enhance model performance across multiple tasks while ensuring robust evaluation methodologies.
In evaluating Chameleon, we focus on tasks requiring text generation conditioned on images, particularly image captioning and visual question-answering, with results grouped by task specificity.
We compared Chameleon to leading models including Flamingo 80B and GPT4-V, noting that we aimed for fidelity in pre-training data over optimizing for zero-shot inference performance.
In image captioning, we employed CiDER scores on various test splits to benchmark Chameleonâs performance against established datasets, ensuring to limit captions appropriately.
The evaluation strategy involved fine-tuning on specific tasks and a multi-task approach to assess how model adjustments impact overall generative capabilities under varying conditions.
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