We first prove the first inequality using Jensen's inequality, which states that for a real-valued, convex Next, we leverage the property of inequalities to prove the second inequality. However, this method has limited effectiveness in scenarios with severe domain shifts between the source and target domains. Directly taking source risk as target risk is unreliable due to domain distribution shifts between domains. This work was completed while Dapeng ( lhxxhb15@gmail.com) Subsequently, Reverse V alidation performs a reversed adaptation from the pseudo-labeled target to the source and utilizes the source risk in this reversed adaptation task for validation.

Existing approaches can be categorized into two types. The first type involves leveraging labeled source data for target-domain model selection [9,14-16]. The second type designs unsupervised metrics based on priors of the learned target-domain structure and utilizes the metrics for model selection[17,19,18,20].

Then, MP A denoises the learned prompts through an auto-encoding process and aligns them by maximizing the agreement of all the reconstructed prompts.

Blended-target domain adaptation (BTDA), which implicitly mixes multiple sub-target domains into a fine domain, has attracted more attention in recent years.

Adaptation Video-text Retrieval (UDA VR), assuming that training (source) data and testing (target) data are from different domains.

In this paper, we present MixV al, an innovative model selection method that operates solely with unlabeled target data during inference.