Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary:  This paper has proposed a method named zero-shot learning for attributes to deal with a research problem about novel attribute classification and attribute labeling. Specifically, this research problem is tackled by first decomposing the seen attributes into basic attributes, and then recombining these basic attributes into new ones. Experiments are conducted on the CUB and α-CLEVR (a synthetic dataset) datasets for empirical evaluation.
strengths_and_weaknesses: - Originality: This paper proposes a novel research problem about novel attribute classification and attributes labeling, which is tackled by zero-shot learning for attributes .

- Quality: In the paper, Sec. 3 details the training of visible attribute detectors with the decomposition-recombination strategy used to synthesize new attribute detectors, providing theoretical support for ZSLA.

- Clarity: The paper is easy to follow and the figures on the paper are straightforwards.

- Significance: The zero-shot problem of attributes is presented and solved, while the proposed method can be used for automatic labeling of attributes.
questions: 1. In Sec. 4.2, the four representative GZSL algorithms are a bit archaic, with the earliest being 2013 and the latest being 2020.
2. There is a problem with the case of the title in the references, for example, in Ln. 390, "Clevr" should be "CLEVR".
3. Several NeurIPS conference papers are incorrectly used in the journal paper format, e.g., [6], [9] and [10].
limitations: In Ln. 257-265, the authors discuss the limitation of the proposed approach that the format of the attribute annotation must be "adjective + object part", e.g., blue wing. However, some ZSL datasets are not labeled with attributes in this form, such as AWA2 and SUN.
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final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The paper proposed to train the model by decomposing overlapping concepts from labeled annotations and then combining them to create new concepts. After training, the model can be used to provide new annotations which can be further used for supervised training. Experimental analyses are provided to explain the results and show improvement over compared methods.
strengths_and_weaknesses: The intuition and motivation are well demonstrated and easy to understand. The results on given comparison settings are reasonable.
questions: 1) My biggest concern is regarding the generalizability of the approach. Taking Figure 1 for example, the model is basically requesting the training data and testing data to be limited to a scope of linear combinations of the two concept sets "red, blue, green" and "head, wing, breast". Would the model totally fail when a new concept is introduced, like either "yellow" or "leg" is they have not been seen during training? That is, is the model "simply" decomposing the existing concepts without really generalizing to real unseen concepts. As the analogy in the title, if the model can really "make an omelette with breaking eggs", that would be generalizing to a relevant concept of omelette from egg, not just different combinations of "brown, white, dark" and "shell, yolk, membrane" under the realm of egg.

2) How is the proposed model related to multi-task image-segmentation models? For example, if we have an encoder-decoder model with different prediction heads for colors and parts, that model should also be able to disentangle two types of attributes and predict accordingly for unseen combinations, and it can even interpolate between attributes by, e.g., providing soft predictions. How is the proposed model better in that sense?

3) I'm not too familiar with the CUB dataset, so it might be helpful to provide SOTA results on CUB for direct comparisons.
limitations: The authors addressed the limitations in Section 4.
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final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: Motivated by the issue of annotation efforts on attribute labels, which are required for zero-shot learning, this paper develops methods to automatically annotate novel attributes for a dataset. Given seen attributes, the proposed method can detect the unseen attributes via a decompose-and-reassemble manner. Results are demonstrated using the CUB dataset alone. As the task of ZSL on attributes is new, most experiments are specified by the authors.
strengths_and_weaknesses: The problem addressed in this paper is new: automatically annotating unseen attributes for zero-shot learning. While the problem is interesting, is it important to drive the ZSL research? Attributes are among one type of semantic information that relate seen and unseen classes. Besides attributes, word embeddings of labels (via word2vec or BERT) can also be used, which can be learned via unsupervised learning and do not require manual annotation. The experiments do not compare the proposed semantic representations (which requires supervision though) to those unsupervised approaches.
Although the proposed decompose-and reassemble approach is fairly sounded, it is not fully validated by the experiments. The experiments were conducted on only one dataset (CUB), which is only one of the five popular datasets in ZSL benchmarks. CUB is fine-grained and challenging, but others (e.g., AWA2, SUN) have different characteristics and are also important.   
questions: As discussed in the limitation of the paper, would the proposed method only work for fine-grained and specific datasets? If it does not work for general, coarse-grained ones like AWA2 and SUN, does it imply this approach has quite limited applications?
limitations: Yes, the authors have provided a paragraph in the beginning of the experimental section to discuss the limitation of the proposed method.
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final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The paper proposes an approach for estimating new detectors of visual attribute conjunctions on images by unmixing its parameters and recombining them. The attribute detector parameter is a prototype in deep feature space that localizes the attributes by cosine similarity. The recombination is produced by averaging the detector parameters. The new attribute synthesis can be used to solve Zero-Shot Classification (ZSC) problems when only few attribute annotations are available in the learning dataset by augmenting the annotations with new synthesized attribute detectors. The approach is compared to 4 other ZSC algorithms on the CUB dataset.

strengths_and_weaknesses: Strengths
- Addressing knowledge transfer at the level of attribute description for classifier design is an interesting idea.
- Rather detailed experiments (distributed b/w main paper and supplementary material) although on a single small dataset.

Weaknesses
- The approach is validated on a single dataset (CUB), limiting its potential usefulness.
- A lot of material is provided in the main paper and supplementary material (17 pages!) but in a confusing organization. For instance, the main algorithm (“intersection”) is not fully described in the main paper, and we need to search for the information in the supplementary material. Wordy general discussions could be replaced fruitfully by more specific technical details.
- The Zero-shot character of the proposed approach seems to me over-stated, and thus misleading: the new attribute synthesis looks like an unmixing/mixing problem, and the unseen category description with the new attributes in fact requires images, which breaks the “zero-shot” principle, as far as I understand it. 

questions: - When reading in the supplementary (C.2) the way the unseen categories are described in $\delta$-CUB dataset from a series of images, it seems to me that what is actually evaluated is more a “few-shot”  than a “zero-shot” learning scheme since images are needed to compute the description: isn’t there a bias when comparing to “pure” GZSL approaches which have to deal with noisy attribute description of unseen categories?  
- I didn’t understand what is the input of the network computing the intersection : is it just an attribute label? If so, how is it encoded? Or is it the detector parameter $m$? 
- Exploiting a VIT architecture seems to me oversized given the complexity of the dataset. Why such a model? 
- The question of building atomic attributes from their conjunctions reminds me of the classical question of blind source separation in signal processing: is there any similarity to that problem?

limitations: Non applicable
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Meta-review: metareview: This paper has proposed a method named zero-shot learning for attributes to deal with a research problem about novel attribute classification and attribute labeling. The reviewers have many questions in the intial round. After the rebuttal, the authours clarify most unclear points, and some reviewers raise the score. In general, all the reviewers agree with the acceptance of this paper.
confidence: Certain
Decision: Accept