Paper: Domain Prompt Matters a Lot in Multi-Source Few-Shot Domain Adaptation (ICLR-2024)

Reviewer comment (Writing):
The overall format of the paper contains many significant problems that prevent clear understanding of the concept, including confusing writing, inconsistency of notations, unclear expressions of novelty, and experimental presentation issues. The paper should be re-written, re-organized, and polished for better reader comprehension.

Author response:
I would like to express my gratitude for your thorough and detailed review of my manuscript. Your careful examination has been instrumental in identifying various issues within my paper. Firstly, in response to your feedback on the writing aspects of the paper, we have systematically reviewed and addressed each of the issues you raised, incorporating necessary modifications.

Reviewer comment (Novelty):
The proposed method in Sec. 3.2 is very similar to DAPL but extended to multi-source scenarios, with the only difference being the introduction of an additional [DOM]. Note that DAPL has not been peer-reviewed.

Author response:
Regarding the novelty concerns raised, I wish to highlight that our primary objective is to introduce a novel training paradigm. By incorporating a domain-aware training mechanism, we aim to provide the model with explicit domain label information for images already present in the domain adaptation dataset. This targeted approach enhances the model's accuracy in prompt learning. Additionally, we propose the concept of "domain-aware mixup" to further improve the model's ability to learn from domain label information in a more refined manner. Through training the model to classify domain information in images containing cross-domain cues, we enable the model to acquire more suitable domain prompts.

Reviewer comment (Writing):
In Eq. 1, the meanings of g() and f() are unclear. They are assumed to be the text and image encoders as in DAPL.

Author response:
Q1.response:  
We appreciate your observation regarding the clarification of functions g() and f() in Equation (1) as the text encoder and image encoder, respectively. In response to this, we have incorporated additional descriptions in the manuscript to explicitly define these functions and enhance reader understanding.

Reviewer comment (Writing):
The relationship between f^i_{mixup} and f_{u} is unclear. They seem to be the same, but neither is used in the subsequent text.

Author response:
Q3.response:  
We acknowledge the writing error in our previous statement, and we appreciate your astute observation. We have rectified this error and supplemented additional descriptions to accurately convey that their essence is indeed the same.

Reviewer comment (Novelty):
The difference between the proposed method and existing works isn't clearly demarcated.

Author response:
In comparison to prior works, our innovation primarily lies in the introduction of [DOM] and the novel training approach. To facilitate a clearer understanding of the innovation behind our method, we have incorporated additional mathematical descriptions. These additions aim to provide readers with a more detailed and comprehensive insight into the unique aspects of our approach.

Reviewer comment (Experiments):
Can the authors provide more ablation experiments on the effects of the three types of prompts?

Author response:
In accordance with your request, we have incorporated additional ablation experiments, providing a more comprehensive comparison with existing works. Furthermore, we have elucidated our improvements through explicit formulation and numerical analysis.

Reviewer comment (Novelty):
What exactly is the differentiation between the current work and previous approaches in terms of formulae and figures presented in the paper?

Author response:
In comparison to prior works, our innovation primarily lies in the introduction of [DOM] and the novel training approach. To facilitate a clearer understanding of the innovation behind our method, we have incorporated additional mathematical descriptions. These additions aim to provide readers with a more detailed and comprehensive insight into the unique aspects of our approach.

Reviewer comment (Experiments):
Would it be possible to test the method on more downstream tasks to evaluate its broader applicability?

Author response:
Thank you for your valuable suggestion. As of the current state of our research, we have not yet explored a broader range of downstream tasks. We appreciate your insight and plan to address this by conducting additional experiments in future research to evaluate the performance across a more extensive set of downstream tasks.

Reviewer comment (Novelty):
The paper falls short in providing a thorough review of related work. The previous works on domain adaptation with prompt learning between 2022 and 2023 are missed. Several works are mentioned above, and thus I suggest the author undertake a more exhaustive literature review and highlight the differences between the proposed method and those previous works.

Author response:
Regarding your suggestion for a comprehensive review of related work, we have diligently examined the references you highlighted and incorporated them into the relevant research section of our paper. In response to your concern about the potential overfitting issue arising from the use of learnable prompts, our investigation was primarily grounded in further experiments inspired by the observations presented in CoCoOp [1] and [2]. The associated problem is briefly addressed in the cited reference, and we acknowledge the need for clarification in our manuscript. We commit to refining this aspect to enhance reader comprehension.

Reviewer comment (Writing):
The writing of the introduction may pose challenges for readers who are new to the subject because the author mentions many terms but without adequate explanation. For example, what are context prompts? What are the differences between the domain and the semantics? What are context variables and domain variables? What is cross-domain mixed feature information?

Author response:
We sincerely apologize for any confusion caused by the lack of clarity in our background introduction. In response to this concern, we have made revisions to enhance clarity, aiming to provide readers with a more accessible understanding of the presented material.

Reviewer comment (Theory):
“Using learnable prompts increases the risk of overfitting on training samples, which reduces the ability of domain prompt models to extract common semantic features.” This is not justified and likely wrong. If training one prompt for a domain does not have the issue, then training multiple prompts respectively for multiple domains should not have the issue as well.

Author response:
Q1.response:
Thank you for your valuable insights. We intended to convey that methods similar to CoOp are susceptible to overfitting, posing challenges in training prompt generation models. This concern has been raised in subsequent works. However, due to clarity issues in our expression, the intended message may not have been effectively communicated. We acknowledge this discrepancy and commit to promptly addressing and clarifying this point in our manuscript.

Reviewer comment (Theory):
“Large-scale pre-trained vision-language models lack domain awareness during training, which hinders the acquisition of domain-specific tendencies necessary for effective domain adaptation tasks.” The general training of a model should not influence its adaptation ability.

Author response:
Q2.response:
In our manuscript, we stated, "Large-scale pre-trained vision-language models lack domain awareness during training, which hinders the acquisition of domain-specific tendencies necessary for effective domain adaptation tasks." I recognize that this statement was unclear and may have led to a misinterpretation. Our intention was to emphasize that in multi-source domain adaptation tasks, where the domain of each image is known, we advocate for the inclusion of domain-specific information in the learning process of large vision-language models. In contrast to approaches that do not consider domain information, such as coop, we conducted a comparative analysis. Our results demonstrate that incorporating domain-aware cues in prompt learning yields improved performance in the context of MFDA.

Reviewer comment (Writing):
“We train the prompt of the domain with this mixed-up feature.” What do you mean “the prompt”? What is "the prompt" referring to?

Author response:
Q4.response:
In addition to the aforementioned clarification, I would like to provide further elucidation regarding the statement, "We train the prompt of the domain with this mixed-up feature," particularly concerning the term "prompt." In our context, the term refers to learnable cues, comprising eight 1024-dimensional vectors. To enhance reader comprehension, we have augmented the manuscript with additional descriptions pertaining to these prompts.

Finally, strict modifications have been made to the proposed format and writing errors。

I hope this clarification addresses your concerns regarding the intended meaning of the mentioned statement. I have made corresponding adjustments in the manuscript to enhance the clarity of this aspect.