Hadamard Product for Low-rank Bilinear Pooling
Bilinear models provide rich representations compared with linear models. They have been applied in various visual tasks, such as object recognition, segmentation, and visual question-answering, to get state-of-the-art performances taking advantage of the expanded representations. However, bilinear representations tend to be high-dimensional, limiting the applicability to computationally complex tasks. We propose low-rank bilinear pooling using Hadamard product for an efficient attention mechanism of multimodal learning. We show that our model outperforms compact bilinear pooling in visual question-answering tasks with the state-of-the-art results on the VQA dataset, having a better parsimonious property.
Results on the VQA task are good for this simple model, the ablation study of table 1 gives some insights as to what is important. Missing are some explanations about the language embedding and the importance in deciding embedding dimension and final output dimension, equivalent to deciding the projected dimension in the compact bilinear model. Since the main contribution of the paper seems to be slightly better performance with fairly large reduction in parameters vs. compact bilinear something should be said about choice of those hyper parameters. If you increase embedded and output dimensions to equalize parameters to the compact bilinear model are further gains possible? How is the question encoded? Is word order preserved in this encoding, the compact bilinear model compared to in table 1 mentions glove, the proposed model is using this as well? The meaning of visual attention in this model along with the number of glimpses should be tied to the sentence embedding, so now we are looking at particular spatial components when that part of the sentence is encoded, then we stack according to your equation 9?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Hadamard Product for Low-rank Bilinear Pooling
Bilinear models provide rich representations compared with linear models. They have been applied in various visual tasks, such as object recognition, segmentation, and visual question-answering, to get state-of-the-art performances taking advantage of the expanded representations. However, bilinear representations tend to be high-dimensional, limiting the applicability to computationally complex tasks. We propose low-rank bilinear pooling using Hadamard product for an efficient attention mechanism of multimodal learning. We show that our model outperforms compact bilinear pooling in visual question-answering tasks with the state-of-the-art results on the VQA dataset, having a better parsimonious property.
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>



Results on the VQA task are good for this simple model, the ablation study of table 1 gives some insights as to what is important. Missing are some explanations about the language embedding and the importance in deciding embedding dimension and final output dimension, equivalent to deciding the projected dimension in the compact bilinear model. Since the main contribution of the paper seems to be slightly better performance with fairly large reduction in parameters vs. compact bilinear something should be said about choice of those hyper parameters. If you increase embedded and output dimensions to equalize parameters to the compact bilinear model are further gains possible? How is the question encoded? Is word order preserved in this encoding, the compact bilinear model compared to in table 1 mentions glove, the proposed model is using this as well? The meaning of visual attention in this model along with the number of glimpses should be tied to the sentence embedding, so now we are looking at particular spatial components when that part of the sentence is encoded, then we stack according to your equation 9?
assistant
Results on the VQA task are good for this simple model, the ablation study of table 1 gives some insights as to what is important. Missing are some explanations about the language embedding and the importance in deciding embedding dimension and final output dimension, equivalent to deciding the projected dimension in the compact bilinear model. Since the main contribution of the paper seems to be slightly better performance with fairly large reduction in parameters vs. compact bilinear something should be said about choice of those hyper parameters. If you increase embedded and output dimensions to equalize parameters to the compact bilinear model are further gains possible? How is the question encoded? Is word order preserved in this encoding, the compact bilinear model compared to in table 1 mentions glove, the proposed model is using this as well? The meaning of visual attention in this model along with the number of glimpses should be tied to the sentence embedding, so now we are looking at particular spatial components when that part of the sentence is encoded, then we stack according to your equation 9?