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Article summary:
1. UniAdapter is a unified parameter-efficient transfer learning method for cross-modal modeling.
2. It reduces the total number of tunable parameters by partially sharing weights between different modalities and their interactions.
3. Experiments on 6 cross-modal downstream benchmarks show that UniAdapter outperforms the state-of-the-arts, even beating full fine-tuning strategies in some cases.
Article analysis:
The article is generally trustworthy and reliable, as it provides evidence to support its claims through extensive experiments on 6 cross-modal downstream benchmarks. The article does not appear to be biased or one-sided, as it presents both sides of the argument equally and objectively. Furthermore, there are no unsupported claims or missing points of consideration in the article. All claims made are backed up with evidence from the experiments conducted, and all possible risks are noted. There are also no promotional content or partiality present in the article. The only potential issue is that there may be unexplored counterarguments that could have been discussed in more detail; however, this does not detract from the overall trustworthiness and reliability of the article.