Few-Shot Learning by Exploiting Object Relation
Few-shot learning trains image classifiers over datasets with few examples per category. It poses challenges for the optimization algorithms, which typically require many examples to fine-tune the model parameters for new categories. Distance-learning-based approaches avoid the optimization issue by embedding the images into a metric space and applying the nearest neighbor classifier for new categories. In this paper, we propose to exploit the object-level relation to learn the image relation feature, which is converted into a distance directly. For a new category, even though its images are not seen by the model, some objects may appear in the training images. Hence, object-level relation is useful for inferring the relation of images from unseen categories. Consequently, our model generalizes well for new categories without fine-tuning. Experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods.
 - In evaluation of omniglot, ("a small improvement over the state-of-the-art should be considered as significant") since all the results are already quite high, the small difference are not much beneficial in practice. Further, to justify the significance of the proposed method, a statistical test should be conducted and reported at least. - In comparison to the LearningToCompare, Sung et al’s method compares the query image to all the support images, while the proposed method compares in a pairwise manner. Thus, from a computational complexity perspective, the proposed method can be much expensive. - The statement of "averaging the object relation features has similar effect as ensemble modeling" needs theoretical or at least empirical supporting results along with explanations.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Few-Shot Learning by Exploiting Object Relation
Few-shot learning trains image classifiers over datasets with few examples per category. It poses challenges for the optimization algorithms, which typically require many examples to fine-tune the model parameters for new categories. Distance-learning-based approaches avoid the optimization issue by embedding the images into a metric space and applying the nearest neighbor classifier for new categories. In this paper, we propose to exploit the object-level relation to learn the image relation feature, which is converted into a distance directly. For a new category, even though its images are not seen by the model, some objects may appear in the training images. Hence, object-level relation is useful for inferring the relation of images from unseen categories. Consequently, our model generalizes well for new categories without fine-tuning. Experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods.
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>



 - In evaluation of omniglot, ("a small improvement over the state-of-the-art should be considered as significant") since all the results are already quite high, the small difference are not much beneficial in practice. Further, to justify the significance of the proposed method, a statistical test should be conducted and reported at least. - In comparison to the LearningToCompare, Sung et al’s method compares the query image to all the support images, while the proposed method compares in a pairwise manner. Thus, from a computational complexity perspective, the proposed method can be much expensive. - The statement of "averaging the object relation features has similar effect as ensemble modeling" needs theoretical or at least empirical supporting results along with explanations.
assistant
 - In evaluation of omniglot, ("a small improvement over the state-of-the-art should be considered as significant") since all the results are already quite high, the small difference are not much beneficial in practice. Further, to justify the significance of the proposed method, a statistical test should be conducted and reported at least. - In comparison to the LearningToCompare, Sung et al’s method compares the query image to all the support images, while the proposed method compares in a pairwise manner. Thus, from a computational complexity perspective, the proposed method can be much expensive. - The statement of "averaging the object relation features has similar effect as ensemble modeling" needs theoretical or at least empirical supporting results along with explanations.