Unsupervised Spatiotemporal Data Inpainting
We tackle the problem of inpainting occluded area in spatiotemporal sequences, such as cloud occluded satellite observations, in an unsupervised manner. We place ourselves in the setting where there is neither access to paired nor unpaired training data. We consider several cases in which the underlying information of the observed sequence in certain areas is lost through an observation operator. In this case, the only available information is provided by the observation of the sequence, the nature of the measurement process and its associated statistics. We propose an unsupervised-learning framework to retrieve the most probable sequence using a generative adversarial network. We demonstrate the capacity of our model to exhibit strong reconstruction capacity on several video datasets such as satellite sequences or natural videos.
This paper proposes a GAN based approach for unsupervised video inpainting. The idea is to learn an auto encoder to reconstruct the unoccluded regions of the frames, and reuse the autoencoder as the generator for a video GAN, which discriminates between real noisy videos and generated videos masked by a known mask distribution. To me the general idea is a straightforward application of GANs to the general inpainting/denoising problem, with the novelty being 1) not relying on paired or unpaired references and 2) extension to the video domain. The main critique I have is that the work seems incomplete and does not show great empirical results, which makes this work a much weaker contribution. Obviously, the authors should complete the experimental results on baseline method 2. It*d also be informative to compare with SOTA unsupervised image inpainting algorithms, if more such baselines are available than video based ones. Another limitation of this work is that a known mask distribution is assumed, while this might be a reasonable assumption in some cases, it can also be easily violated in real world problems. It*d be great improvement if such an assumption can be relaxed, for example, assuming a family of mask distributions but not the exact instantiation. Overall I think this work attempts to solve an interesting problem with an incremental but reasonable approach, and more empirical evaluation is needed to make the paper meet the bar for acceptance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unsupervised Spatiotemporal Data Inpainting
We tackle the problem of inpainting occluded area in spatiotemporal sequences, such as cloud occluded satellite observations, in an unsupervised manner. We place ourselves in the setting where there is neither access to paired nor unpaired training data. We consider several cases in which the underlying information of the observed sequence in certain areas is lost through an observation operator. In this case, the only available information is provided by the observation of the sequence, the nature of the measurement process and its associated statistics. We propose an unsupervised-learning framework to retrieve the most probable sequence using a generative adversarial network. We demonstrate the capacity of our model to exhibit strong reconstruction capacity on several video datasets such as satellite sequences or natural videos.
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 proposes a GAN based approach for unsupervised video inpainting. The idea is to learn an auto encoder to reconstruct the unoccluded regions of the frames, and reuse the autoencoder as the generator for a video GAN, which discriminates between real noisy videos and generated videos masked by a known mask distribution. To me the general idea is a straightforward application of GANs to the general inpainting/denoising problem, with the novelty being 1) not relying on paired or unpaired references and 2) extension to the video domain. The main critique I have is that the work seems incomplete and does not show great empirical results, which makes this work a much weaker contribution. Obviously, the authors should complete the experimental results on baseline method 2. It*d also be informative to compare with SOTA unsupervised image inpainting algorithms, if more such baselines are available than video based ones. Another limitation of this work is that a known mask distribution is assumed, while this might be a reasonable assumption in some cases, it can also be easily violated in real world problems. It*d be great improvement if such an assumption can be relaxed, for example, assuming a family of mask distributions but not the exact instantiation. Overall I think this work attempts to solve an interesting problem with an incremental but reasonable approach, and more empirical evaluation is needed to make the paper meet the bar for acceptance.
assistant
This paper proposes a GAN based approach for unsupervised video inpainting. The idea is to learn an auto encoder to reconstruct the unoccluded regions of the frames, and reuse the autoencoder as the generator for a video GAN, which discriminates between real noisy videos and generated videos masked by a known mask distribution. To me the general idea is a straightforward application of GANs to the general inpainting/denoising problem, with the novelty being 1) not relying on paired or unpaired references and 2) extension to the video domain. The main critique I have is that the work seems incomplete and does not show great empirical results, which makes this work a much weaker contribution. Obviously, the authors should complete the experimental results on baseline method 2. It*d also be informative to compare with SOTA unsupervised image inpainting algorithms, if more such baselines are available than video based ones. Another limitation of this work is that a known mask distribution is assumed, while this might be a reasonable assumption in some cases, it can also be easily violated in real world problems. It*d be great improvement if such an assumption can be relaxed, for example, assuming a family of mask distributions but not the exact instantiation. Overall I think this work attempts to solve an interesting problem with an incremental but reasonable approach, and more empirical evaluation is needed to make the paper meet the bar for acceptance.