Dynamic Scale Inference by Entropy Minimization
Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field. Rather than enumerate variations across filter channels or pyramid levels, dynamic models locally predict scale and adapt receptive fields accordingly. The degree of variation and diversity of inputs makes this a difficult task. Existing methods either learn a feedforward predictor, which is not itself totally immune to the scale variation it is meant to counter, or select scales by a fixed algorithm, which cannot learn from the given task and data. We extend dynamic scale inference from feedforward prediction to iterative optimization for further adaptivity. We propose a novel entropy minimization objective for inference and optimize over task and structure parameters to tune the model to each input. Optimization during inference improves semantic segmentation accuracy and generalizes better to extreme scale variations that cause feedforward dynamic inference to falter.
The authors propose a method to dynamically adapt some structural features of a semantic image segmentation model at inference time based on the entropy of the predictions. Using a model that explicitly controls the size of the filters at each layer, they show that running a small number of SGD steps on the scale and final prediction parameters in the last layer to minimize the entropy of least confident predictions for a specific example leads to better performance overall, and especially better generalization when there is a size discrepancy between training and test set. Strengths: The method is inspired, and leads to significant improvements. The dynamic inference setup is clearly explained, and well motivated for the case of the scale parameters. Extensive ablation experiments and the inclusion of an oracle system help understand the contributions of each component of the setup, and the potential of inference-time optimization of the considered parameters. Weaknesses: Some information is missing from the description of the experimental setting. A quick review of the DLA model would be welcome, to get a better sense of the roles of 	heta_{scale} and 	heta_{score}. The authors should include published numbers for a relevant baseline and the current or recent state-of-the-art for the considered dataset (Table 1should also report 1x numbers in both settings). Finally, while the authors make a strong case for dynamic adaptation of the scale parameters, the prior reason for adapting 	heta_{score} is less obvious and would require further explanation. Questions and miscellaneous remarks: “As reported in Table 1, our method consistently improves on the baseline by ?2 points for all scales” > this statement is a little misleading, since the improvement is ~2 points on average, not ~2 points for each scale. Wouldn’t simply multiplying 	heta_{score} by a large number decrease the entropy of the predictions? Do you do anything to prevent that from happening? Similarly, couldn’t the adversary simply rotate 	heta_{score} to reduce IoU? Is the adversary optimized for long enough?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Dynamic Scale Inference by Entropy Minimization
Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field. Rather than enumerate variations across filter channels or pyramid levels, dynamic models locally predict scale and adapt receptive fields accordingly. The degree of variation and diversity of inputs makes this a difficult task. Existing methods either learn a feedforward predictor, which is not itself totally immune to the scale variation it is meant to counter, or select scales by a fixed algorithm, which cannot learn from the given task and data. We extend dynamic scale inference from feedforward prediction to iterative optimization for further adaptivity. We propose a novel entropy minimization objective for inference and optimize over task and structure parameters to tune the model to each input. Optimization during inference improves semantic segmentation accuracy and generalizes better to extreme scale variations that cause feedforward dynamic inference to falter.
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>



The authors propose a method to dynamically adapt some structural features of a semantic image segmentation model at inference time based on the entropy of the predictions. Using a model that explicitly controls the size of the filters at each layer, they show that running a small number of SGD steps on the scale and final prediction parameters in the last layer to minimize the entropy of least confident predictions for a specific example leads to better performance overall, and especially better generalization when there is a size discrepancy between training and test set. Strengths: The method is inspired, and leads to significant improvements. The dynamic inference setup is clearly explained, and well motivated for the case of the scale parameters. Extensive ablation experiments and the inclusion of an oracle system help understand the contributions of each component of the setup, and the potential of inference-time optimization of the considered parameters. Weaknesses: Some information is missing from the description of the experimental setting. A quick review of the DLA model would be welcome, to get a better sense of the roles of 	heta_{scale} and 	heta_{score}. The authors should include published numbers for a relevant baseline and the current or recent state-of-the-art for the considered dataset (Table 1should also report 1x numbers in both settings). Finally, while the authors make a strong case for dynamic adaptation of the scale parameters, the prior reason for adapting 	heta_{score} is less obvious and would require further explanation. Questions and miscellaneous remarks: “As reported in Table 1, our method consistently improves on the baseline by ?2 points for all scales” > this statement is a little misleading, since the improvement is ~2 points on average, not ~2 points for each scale. Wouldn’t simply multiplying 	heta_{score} by a large number decrease the entropy of the predictions? Do you do anything to prevent that from happening? Similarly, couldn’t the adversary simply rotate 	heta_{score} to reduce IoU? Is the adversary optimized for long enough?
assistant
The authors propose a method to dynamically adapt some structural features of a semantic image segmentation model at inference time based on the entropy of the predictions. Using a model that explicitly controls the size of the filters at each layer, they show that running a small number of SGD steps on the scale and final prediction parameters in the last layer to minimize the entropy of least confident predictions for a specific example leads to better performance overall, and especially better generalization when there is a size discrepancy between training and test set. Strengths: The method is inspired, and leads to significant improvements. The dynamic inference setup is clearly explained, and well motivated for the case of the scale parameters. Extensive ablation experiments and the inclusion of an oracle system help understand the contributions of each component of the setup, and the potential of inference-time optimization of the considered parameters. Weaknesses: Some information is missing from the description of the experimental setting. A quick review of the DLA model would be welcome, to get a better sense of the roles of 	heta_{scale} and 	heta_{score}. The authors should include published numbers for a relevant baseline and the current or recent state-of-the-art for the considered dataset (Table 1should also report 1x numbers in both settings). Finally, while the authors make a strong case for dynamic adaptation of the scale parameters, the prior reason for adapting 	heta_{score} is less obvious and would require further explanation. Questions and miscellaneous remarks: “As reported in Table 1, our method consistently improves on the baseline by ?2 points for all scales” > this statement is a little misleading, since the improvement is ~2 points on average, not ~2 points for each scale. Wouldn’t simply multiplying 	heta_{score} by a large number decrease the entropy of the predictions? Do you do anything to prevent that from happening? Similarly, couldn’t the adversary simply rotate 	heta_{score} to reduce IoU? Is the adversary optimized for long enough?