Understanding image motion with group representations
Motion is an important signal for agents in dynamic environments, but learning to represent motion from unlabeled video is a difficult and underconstrained problem. We propose a model of motion based on elementary group properties of transformations and use it to train a representation of image motion. While most methods of estimating motion are based on pixel-level constraints, we use these group properties to constrain the abstract representation of motion itself. We demonstrate that a deep neural network trained using this method captures motion in both synthetic 2D sequences and real-world sequences of vehicle motion, without requiring any labels. Networks trained to respect these constraints implicitly identify the image characteristic of motion in different sequence types. In the context of vehicle motion, this method extracts information useful for localization, tracking, and odometry. Our results demonstrate that this representation is useful for learning motion in the general setting where explicit labels are difficult to obtain.
 Paper proposes an approach for learning video motion features in an unsupervised manner. A number of constraints are used to optimize the neural network that consists of CNN + RNN (LSTM). Constraints stem from group structure of sequences and include associativity and inevitability. For example, forward-backward motions should cancel each other out and motions should be additive. Optimized network is illustrated to produce features that can be used to regress odometry. Overall the approach is interesting from the conceptual point of view, however, experimental validation is very preliminary. This makes it difficult to asses the significance and viability of the approach. In particular, the lack of direct comparison, makes it difficult to asses whether the proposed group constraints are competitive with brightness constancy (or similar) constraints used to learn motion in an unsupervised manner in other papers. It is true that proposed model may be able to learn less local motion information, but it is not clear if this is what happens in practice. In order to put the findings in perspective authors should compare to unsupervised optical flow approach (e.g., unsupervised optical flow produced by one of the proposed CNN networks and used to predict odometer on KITTI for fair comparison). Without a comparison of this form the paper is incomplete and the findings are difficult to put in the context of state-of-the-art. Also, saying that learned features can predict odometry “better than chance” (Section 4.2 and Table 2) seems like a pretty low bar for a generic feature representation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Understanding image motion with group representations
Motion is an important signal for agents in dynamic environments, but learning to represent motion from unlabeled video is a difficult and underconstrained problem. We propose a model of motion based on elementary group properties of transformations and use it to train a representation of image motion. While most methods of estimating motion are based on pixel-level constraints, we use these group properties to constrain the abstract representation of motion itself. We demonstrate that a deep neural network trained using this method captures motion in both synthetic 2D sequences and real-world sequences of vehicle motion, without requiring any labels. Networks trained to respect these constraints implicitly identify the image characteristic of motion in different sequence types. In the context of vehicle motion, this method extracts information useful for localization, tracking, and odometry. Our results demonstrate that this representation is useful for learning motion in the general setting where explicit labels are difficult to obtain.
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>



 Paper proposes an approach for learning video motion features in an unsupervised manner. A number of constraints are used to optimize the neural network that consists of CNN + RNN (LSTM). Constraints stem from group structure of sequences and include associativity and inevitability. For example, forward-backward motions should cancel each other out and motions should be additive. Optimized network is illustrated to produce features that can be used to regress odometry. Overall the approach is interesting from the conceptual point of view, however, experimental validation is very preliminary. This makes it difficult to asses the significance and viability of the approach. In particular, the lack of direct comparison, makes it difficult to asses whether the proposed group constraints are competitive with brightness constancy (or similar) constraints used to learn motion in an unsupervised manner in other papers. It is true that proposed model may be able to learn less local motion information, but it is not clear if this is what happens in practice. In order to put the findings in perspective authors should compare to unsupervised optical flow approach (e.g., unsupervised optical flow produced by one of the proposed CNN networks and used to predict odometer on KITTI for fair comparison). Without a comparison of this form the paper is incomplete and the findings are difficult to put in the context of state-of-the-art. Also, saying that learned features can predict odometry “better than chance” (Section 4.2 and Table 2) seems like a pretty low bar for a generic feature representation.
assistant
 Paper proposes an approach for learning video motion features in an unsupervised manner. A number of constraints are used to optimize the neural network that consists of CNN + RNN (LSTM). Constraints stem from group structure of sequences and include associativity and inevitability. For example, forward-backward motions should cancel each other out and motions should be additive. Optimized network is illustrated to produce features that can be used to regress odometry. Overall the approach is interesting from the conceptual point of view, however, experimental validation is very preliminary. This makes it difficult to asses the significance and viability of the approach. In particular, the lack of direct comparison, makes it difficult to asses whether the proposed group constraints are competitive with brightness constancy (or similar) constraints used to learn motion in an unsupervised manner in other papers. It is true that proposed model may be able to learn less local motion information, but it is not clear if this is what happens in practice. In order to put the findings in perspective authors should compare to unsupervised optical flow approach (e.g., unsupervised optical flow produced by one of the proposed CNN networks and used to predict odometer on KITTI for fair comparison). Without a comparison of this form the paper is incomplete and the findings are difficult to put in the context of state-of-the-art. Also, saying that learned features can predict odometry “better than chance” (Section 4.2 and Table 2) seems like a pretty low bar for a generic feature representation.