Comments (2)
Hi, Thanks for your notice.
In the ViTPose paper, the results are reported using the multi-task training setting. In our ViTPose++ paper (Table 15), the ViTPose results indicate the model trained with a single dataset (MS COCO in this case).
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Hi @Annbless,
Thank you for your prompt and clear explanation. I realize now that I overlooked this detail when comparing the data. This clarification will surely be beneficial for anyone delving into the nuances of your impactful work.
Thanks again for your guidance and contributions to the field.
Reference
To assist others who might have similar questions in the future, I've extracted the relevant text from both papers.
From the ViTPose paper, section A:
"Please note that the ViTPose variants are trained under the multi-dataset training setting and tested directly without further finetuning on the specific training dataset, to keep the whole pipeline as simple as possible."
From the ViTPose++ paper, section 4.5.2:
"Note that the ViTPose++ models are trained with the combination of all the datasets and directly tested on the target dataset without further fine-tuning, which keeps the whole pipeline as simple as possible. For each dataset, we use the corresponding FFN, decoder, and prediction head in ViTPose for prediction. We also provide the ViTPose baseline results. It’s worth highlighting that, despite using the same number of parameters for inference, ViTPose++ utilizes much fewer parameters during training compared with training individual ViTPose models for each dataset."
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Related Issues (20)
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