Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposed to use meta learning for multimodal few-shot learning. Unlike previous methods that may train fully or partially the vision or language models, this paper freezes both vision and language models while only training the learnable parameters for the meta-mapper. Thanks to the frozen backbones, the meta-learning process is fast and able to make use of the pre-trained knowledge. The experimental results show that the proposed method outperformed the existing Frozen multimodal learning baseline. 

strength_and_weaknesses: Strength: 
* The idea itself is very simple and easy to understand, for an impactful research direction. Making use of already pre-trained vision/language models could help save computational cost.
* This paper is very well written and is very easy to understand. The illustrations of the idea are very clear. 
* Good qualitative results and ablation tests.

Weaknesses:
* It is appreciated to mark both "episodic" and "cross-domain" in Table 1 and Table 2. It is clear that the proposed "episodic" training helps on the quality significantly on target tasks. However, "cross-domain" leads to unfair comparisons, where the Frozen baseline only reported results with "cross-domain" setup while this paper highlights results with non "cross-domain" ("in-domain") setups. I am concerned by this point and if we look at "episodic" and "cross-domain" row in Table 2, this paper falls behind Frozen overall. 

clarity,_quality,_novelty_and_reproducibility: This paper is very well written and is very easy to understand. The frozen backbone idea itself has been explored by a few existing methods, e.g. freezing the language backbone (Frozen). Adopting meta-learning to bridge both vision and language backbone is original, and it also works nicely on the benchmarks. This paper has not attached/released the code, which makes it non-trivial to be reproduced.

summary_of_the_review: This paper proposed a simple idea to bridge pre-trained vision and language backbones via meta-learning. The training is parameter efficient and fast thanks to frozen backbones. Concerns regarding the "cross-domain" comparisons lead to borderline rating.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper proposes a new approach to few-shot multimodal learning using a meta-learning approach that relies on generating visual prefixes that leverage prior training with other tasks. The visual prefix is produced by the proposed meta-mapper that meta-learns from previous tasks. The prefix approach enables seamless bridging of the textual and visual modalities. The proposed approach is computationally lightweight since it uses frozen backbone visual and text encoders (i.e. does not require retraining those), and clearly advances over the state of the art in its results. The paper also presents several insightful ablation results that establish the significance of the proposed approach to meta-learning.
strength_and_weaknesses: Strengths:
1. Thorough and insightful literature survey that provides a sound motivation for the proposed approach.
2. Clear explanation of both the proposed approach and the experimental results.
3. Sound technical approach that is computationally lightweight and applicable to multiple multimodal tasks.
4. Experimental results show clear advance over the state of the art.
5. Insightful ablation results show the advantage of the proposed approach.

Weaknesses
1. No significant weaknesses. A suggestion - have you thought of how you would bridge three modalities? That is not required for this paper but I would be curious to know your thoughts. For example, how would you bridge visual, text and audio modalities? My guess is that your approach will scale up easily to accomodate more modalities but do think about it.
clarity,_quality,_novelty_and_reproducibility: Clarity
The paper is well written. It builds the approach logically based on a thorough literature survey and presents the proposed approach abd results clearly and concisely.
Quality and originality

The authors claim to be the first to apply meta-learning to multimodal few shot learning. That is a justified claim and the authors follow it up with good experimental results in both accuracy and ablation. I would therefore rate the quality and originality of this work highly. 
summary_of_the_review: Well written paper. Clear advancement of the state of the art with both the approach and experimental results. Insightful results from ablation. Good paper.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper propose a meta mapper network to tackle the task of multi-modal few-shot learning. Particularly, the proposed meta mapper contextualizes the soft visual prompts with the features of a pre-trained/frozen visual encoder and language models, for adapting the language model to output for the target task. During the inference, only the meta-mapper is fine-tuned via gradient based optimization, to adapt the model for the target multi-modal learning tasks. To train this meta-mapper, MAML-style of meta learning routine is employed for training. With this key idea, this paper achieves strong results on Real-Name mini-ImageNet and Open-Ended mini-ImageNet.
strength_and_weaknesses: Strength:

+ The main idea of combining soft-prompt tuning and MAML is very straightforward, and novel to some extent.
+ The empirical results are strong, and show that the proposed method outperforms the previous state-of-the-art in multimodal few-shot learning.
+ The ablation studies are quite comprehensive, and most of my questions about the methods was addressed. 

Weakness:
- I don't see very obvious weakness of the methods. 

Questions:

One question I have is about the variance of the model's performance, with respect to the few-shot support examples. Figure 3 has shown same hints but it could be interesting to more variance analysis on realistic VQA data. 
clarity,_quality,_novelty_and_reproducibility: The overall writing quality is pretty clear, methods are well-motivated. 

The overall quality of the paper is good, solid and comprehensive experiments, and the novelty is okay. 

I believe the methods are reproducible. 
summary_of_the_review: Overall I think this paper is making a solid contribution to the multi-model few-shot learning. The proposed method is interesting enough and effective.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: This paper proposes to combine meta learning with few-shot adaptation of language models, by essentially building upon methods such as Frozen (Tsimpoukelli et al., 2021) which maps image representations into language model token space, but adding a meta-mapper network, acting as a meta-learner, to more efficiently perform this mapping. The proposed approach can rapidly adapt to newly presented samples with only a few gradient updates, and shows strong results on recently proposed multimodal few-shot benchmarks.
strength_and_weaknesses: Strengths:
1. The paper is well motivated and described. The ideas are clear and experiments are on several large multimodal datasets.
2. The paper is very clear with clear figures and exposition.
3. Experiments are comprehensive and results are strong.

Weaknesses:
1. The novelty of the paper is limited, since it seems to be a combination of Frozen (Tsimpoukelli et al., 2021) and standard meta-learning approaches to learn the mapping from image representations into language model token space.
2. There should be more comparisons in both performance and complexity - the proposed method does better but certainly uses more computation so the tradeoff should be analyzed.
clarity,_quality,_novelty_and_reproducibility: Paper is very clear and nicely written - was a joy to read, but the originality is lacking.
summary_of_the_review: The novelty of the paper is limited, since it seems to be a combination of Frozen (Tsimpoukelli et al., 2021) and standard meta-learning approaches to learn the mapping from image representations into language model token space.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: On the basis of the average score, this may seem like a borderline paper, however barring the review of Reviewer zhwM (score 3) the remaining reviews range from sympathy to outright support for publication. Focussing on Reviewer zhwM's comments, it seems the main concern there is the issue of novelty, with the reviewer contending that the proposed work resembled the method from Frozen (Tsimpoukelli et al., 2021) applied to a different domain (which some might argue, were this the case, that this is novel). The reviewer gave a detailed review, replied to most author responses, was cordial and not hostile, so I do not wish to completely discount their criticism. My main reasons for weighing it less in my decision are:
* Although an argument for novelty can be made, such matters are highly subjective, as the reviewer points out. The rest of their comments about the paper are actually quite positive, and so while I would feel comfortable seeing a score of, say, 5 associated with this review, a 3 seems low.
* Furthermore, I think the authors have responded to the points the reviewer brings up, especially regarding the conditions under which they would raise their score. Unfortunately, the reviewer was not able to reply to these final comments, so it is not known whether or not they would indeed raise their score, but from reading the response myself I think the benefit of doubt leans towards assuming they would temper the depth or confidence of their negative feedback.

As such, on the balance of the reviews and arguments made, I think this paper is entirely acceptable to the conference.
justification_for_why_not_higher_score: If we were to pass on the most negative review (see argument above) and look at the positive case, the paper is decently well supported by the reviewers. I don't think there's enough enthusiasm to carry this to a spotlight, but wouldn't object if it was bumped up.
justification_for_why_not_lower_score: See review summary.
Decision: Accept: poster