CLAREL: classification via retrieval loss for zero-shot learning
We address the problem of learning fine-grained cross-modal representations. We propose an instance-based deep metric learning approach in joint visual and textual space. The key novelty of this paper is that it shows that using per-image semantic supervision leads to substantial improvement in zero-shot performance over using class-only supervision. On top of that, we provide a probabilistic justification for a metric rescaling approach that solves a very common problem in the generalized zero-shot learning setting, i.e., classifying test images from unseen classes as one of the classes seen during training. We evaluate our approach on two fine-grained zero-shot learning datasets: CUB and FLOWERS. We find that on the generalized zero-shot classification task CLAREL consistently outperforms the existing approaches on both datasets.
This paper presents two main contributions: first, a simple retrieval-based objective for learning joint text and image representations, and second, a metric-scaling term that improves performance on unseen classes in the generalized zero-shot (GZSL) setting. They evaluate on two datasets, CUB and FLOWERS, consisting of images paired with text descriptions, and show that their relatively simple technique outperforms complex GAN and VAE-based models. Weak accept, see considerations below. The results seem solid, and the architecture is quite simple compared to other models on this task. However, there are some gaps in the analysis, and the paper would benefit from a more careful comparison to the literature. The results are strong, and it’s encouraging to see a simple approach like this outperform more complex models. However, it’s not clear how novel the retrieval model is; as implemented, it’s similar to a sampled softmax or noise contrastive estimation - the paper would benefit from a more careful comparison of their approach to established alternatives, and a discussion of why this approach should learn better representations than a VAE or GAN. It’s also not clear how novel the metric scaling technique is. It undeniably works well, but the use seems remarkably similar to (Das & Lee 2019, Section II.D). The observation (Section 4.5) that the retrieval-only model performs well without any class labels is interesting. How does this compare to other unsupervised approaches? It also appears that the version with the metric scaling here still performs better - is this because the retrieval model is trained only on examples from the “seen” classes? Does this gap go away if (unlabeled) examples from the unseen classes are available at training time? A few other notes: * Section 4.5: “Our interpretation is that the addition of the image/text classifier loss helps to reduce the intraclass variability in embeddings and provides for tighter clustering.” - could you test this directly, or provide some visualization, such as t-SNE plots? * The image representations are initialized from an ImageNet model, but text is trained from scratch. Why not use a pretrained text encoder like BERT? * Table 3: it would help to have text labels for these rows, or a reminder in the caption of what lambda = 0 and kappa = 0 mean (otherwise, one needs to flip/scroll back to page 4) * Figure 2: what are the actual values of u and s that contribute to this plot?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
CLAREL: classification via retrieval loss for zero-shot learning
We address the problem of learning fine-grained cross-modal representations. We propose an instance-based deep metric learning approach in joint visual and textual space. The key novelty of this paper is that it shows that using per-image semantic supervision leads to substantial improvement in zero-shot performance over using class-only supervision. On top of that, we provide a probabilistic justification for a metric rescaling approach that solves a very common problem in the generalized zero-shot learning setting, i.e., classifying test images from unseen classes as one of the classes seen during training. We evaluate our approach on two fine-grained zero-shot learning datasets: CUB and FLOWERS. We find that on the generalized zero-shot classification task CLAREL consistently outperforms the existing approaches on both datasets.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presents two main contributions: first, a simple retrieval-based objective for learning joint text and image representations, and second, a metric-scaling term that improves performance on unseen classes in the generalized zero-shot (GZSL) setting. They evaluate on two datasets, CUB and FLOWERS, consisting of images paired with text descriptions, and show that their relatively simple technique outperforms complex GAN and VAE-based models. Weak accept, see considerations below. The results seem solid, and the architecture is quite simple compared to other models on this task. However, there are some gaps in the analysis, and the paper would benefit from a more careful comparison to the literature. The results are strong, and it’s encouraging to see a simple approach like this outperform more complex models. However, it’s not clear how novel the retrieval model is; as implemented, it’s similar to a sampled softmax or noise contrastive estimation - the paper would benefit from a more careful comparison of their approach to established alternatives, and a discussion of why this approach should learn better representations than a VAE or GAN. It’s also not clear how novel the metric scaling technique is. It undeniably works well, but the use seems remarkably similar to (Das & Lee 2019, Section II.D). The observation (Section 4.5) that the retrieval-only model performs well without any class labels is interesting. How does this compare to other unsupervised approaches? It also appears that the version with the metric scaling here still performs better - is this because the retrieval model is trained only on examples from the “seen” classes? Does this gap go away if (unlabeled) examples from the unseen classes are available at training time? A few other notes: * Section 4.5: “Our interpretation is that the addition of the image/text classifier loss helps to reduce the intraclass variability in embeddings and provides for tighter clustering.” - could you test this directly, or provide some visualization, such as t-SNE plots? * The image representations are initialized from an ImageNet model, but text is trained from scratch. Why not use a pretrained text encoder like BERT? * Table 3: it would help to have text labels for these rows, or a reminder in the caption of what lambda = 0 and kappa = 0 mean (otherwise, one needs to flip/scroll back to page 4) * Figure 2: what are the actual values of u and s that contribute to this plot?
assistant
This paper presents two main contributions: first, a simple retrieval-based objective for learning joint text and image representations, and second, a metric-scaling term that improves performance on unseen classes in the generalized zero-shot (GZSL) setting. They evaluate on two datasets, CUB and FLOWERS, consisting of images paired with text descriptions, and show that their relatively simple technique outperforms complex GAN and VAE-based models. Weak accept, see considerations below. The results seem solid, and the architecture is quite simple compared to other models on this task. However, there are some gaps in the analysis, and the paper would benefit from a more careful comparison to the literature. The results are strong, and it’s encouraging to see a simple approach like this outperform more complex models. However, it’s not clear how novel the retrieval model is; as implemented, it’s similar to a sampled softmax or noise contrastive estimation - the paper would benefit from a more careful comparison of their approach to established alternatives, and a discussion of why this approach should learn better representations than a VAE or GAN. It’s also not clear how novel the metric scaling technique is. It undeniably works well, but the use seems remarkably similar to (Das & Lee 2019, Section II.D). The observation (Section 4.5) that the retrieval-only model performs well without any class labels is interesting. How does this compare to other unsupervised approaches? It also appears that the version with the metric scaling here still performs better - is this because the retrieval model is trained only on examples from the “seen” classes? Does this gap go away if (unlabeled) examples from the unseen classes are available at training time? A few other notes: * Section 4.5: “Our interpretation is that the addition of the image/text classifier loss helps to reduce the intraclass variability in embeddings and provides for tighter clustering.” - could you test this directly, or provide some visualization, such as t-SNE plots? * The image representations are initialized from an ImageNet model, but text is trained from scratch. Why not use a pretrained text encoder like BERT? * Table 3: it would help to have text labels for these rows, or a reminder in the caption of what lambda = 0 and kappa = 0 mean (otherwise, one needs to flip/scroll back to page 4) * Figure 2: what are the actual values of u and s that contribute to this plot?