Many significant problems are found in the current format of the paper that prevents the understanding of the concept. The problem includes but is not limited to confusing writing, inconsistency of notations, expressions of novelty, experimental presentation etc. It is highly recommended that the authors to re-write the paper, re-organize the content and better polish the text for the reader to better understand.
The major problems:
* Novelty is limited:
- The proposed method as in Sec. 3.2 is very similar to DAPL but extended to multi-source scenarios. The only difference is to introduce an additional [DOM]. Note, that DAPL has not been peer-reviewed.
- The motivation for domain-aware mixup is confusing. I cannot be convinced and do not understand in the current writing, how it can enforce to learn domain-specific knowledge. The corresponding literature regarding mixup in the feature space is also not referenced and discussed (e.g. [1]).
- The description for deriving the domain-aware mixup is confusing. I assume the authors are trying to develop a method so that the learned prompt shares the knowledge between source and target domains (depending on Eq. 9)?
* Writing:
- In the first sentence of Abstract: “large vision-language models … strong performance in MFDA”. There is no such reference applying large VL models in MFDA. In fact, MFDA is a rarely studied problem.
- The description of the problem setting (MFDA) should be clearly explained at the beginning (abstract or introduction) so that the reader can refer better to the limitations of prior works.
- Paragraphs 1 & 2 in the introduction: the connection is missing, and ‘prompt learning’ suddenly jumps in, making the concept broken.
- Fig. 1 is not referred to in the paper.
- Related work: after describing the related prior works of each field, it's suggested to write a couple of sentences to distinguish between them to show the novelty of the proposed method.
- The description of the MFDA setting is very confusing in the first paragraph of the Method Section: “single target domain with \textbf{sparse} labels”, “…target distribution p_T(x, y) with label observation…” is mentioned, but the notation for target domain \tau is unlabeled. In the original MFDA paper (Yue et al., 2021a), the target data is unlabeled. What about the unlabeled data in source domains? Are they used during training (as in (Yue et al., 2021a))? It is very confusing that the problem setting description defers significantly as in (Yue et al., 2021a).
- There is significant text overlapping with DAPL in the preliminary sections of both papers (only with some rewording..). It should be strictly prohibited.
- What is [DOM] in Eq. 4? I assume it is a domain ID? And I assume [DOM] is the non-learnable component near the description of Eq. 4?
- Notation: what is subscript d in Eq. 4 and superscript d in Eq. 5? They are not explained in the text. I assume they are the domain IDs?
- What does it mean by ‘d*k categories’ as in the sentence after Eq. 5?
- Eq. 6 is very confusing. For the outer summation on d \in {s, u}, what is the purpose of computing the similarity between the target domain prompt and source image features? How does the learning on unlabeled target data is realized?
- What is inter-source domain mixup? In the current format of writing, I don’t understand why maintaining it will harm the representation learning on the target domain. The motivation is weak.
- In the second paragraph on page 6, the notation of target domain data y_t is different from Section 3.
- In Fig. 3, letters v and f are used to represent the features of “painting” and “real”. But v is used to represent text prompts as in Eq. 3
- The feature mix-up formulation in Fig. 3 is different than Eq. 8. One uses \gamma and another one uses \lambda? and the weighting is different?
- It is really confusing that the letter “t” is used to refer to text and target domain.
- What are D^s and D^u in Eq. 10? They are never defined. I assume they are source and target domains, which is inconsistent with what is described in the problem setting. The problem setting is borrowed from (Yue et al., 2021a). But Eq. 10 is copied from DAPL paper. Please keep everything consistent throughout the paper. Also, Eq. 9 requires source data as well, why only D^u is passed to L_u as in Eq. 10?
- The notations for loss functions in Eq. 7, 9, and 10 should be consistent.
- Table 5 in the last sentence of Page 8 should be Figure 5.
- The experimental setting/comparison is very confusing. What is “single best”, which can be both setting and method as in Table 1&2? What is source combined? Which rows in Tables 1&2 refer to the MFDA? How come the “Large model” in Table 1&2 can be the setting, it should be the model architecture.
- For Figure 6&7, they are hard to see the differences. It is suggested to use a table to report the numbers.
[1] Adversarial Domain Adaptation with Domain Mixup. AAAI 2020.
- The description of the MFDA setting is very confusing in the first paragraph of the Method Section: “single target domain with \textbf{sparse} labels”, “…target distribution p_T(x, y) with label observation…” is mentioned, but the notation for target domain \tau is unlabeled. In the original MFDA paper (Yue et al., 2021a), the target data is unlabeled. What about the unlabeled data in source domains? Are they used during training (as in (Yue et al., 2021a))? It is very confusing that the problem setting description defers significantly as in (Yue et al., 2021a).