High-Frequency guided Curriculum Learning for Class-specific Object Boundary Detection
This work addresses class-specific object boundary extraction, i.e., retrieving boundary pixels that belong to a class of objects in the given image. Although recent ConvNet-based approaches demonstrate impressive results, we notice that they produce several false-alarms and misdetections when used in real-world applications. We hypothesize that although boundary detection is simple at some pixels that are rooted in identifiable high-frequency locations, other pixels pose a higher level of difficulties, for instance, region pixels with an appearance similar to the boundaries; or boundary pixels with insignificant edge strengths. Therefore, the training process needs to account for different levels of learning complexity in different regions to overcome false alarms. In this work, we devise a curriculum-learning-based training process for object boundary detection. This multi-stage training process first trains the network at simpler pixels (with sufficient edge strengths) and then at harder pixels in the later stages of the curriculum. We also propose a novel system for object boundary detection that relies on a fully convolutional neural network (FCN) and wavelet decomposition of image frequencies. This system uses high-frequency bands from the wavelet pyramid and augments them to conv features from different layers of FCN. Our ablation studies with contourMNIST dataset, a simulated digit contours from MNIST, demonstrate that this explicit high-frequency augmentation helps the model to converge faster. Our model trained by the proposed curriculum scheme outperforms a state-of-the-art object boundary detection method by a significant margin on a challenging aerial image dataset.
The main idea of the paper is adding a curriculum learning-based extension to CASEnet, a boundary detection method from 2017. In the first phase, the loss emphasizes easier examples with high gradient in the image, and in the second phase, the method is trained on all boundary pixels. This change seems to improve edge detection performance on a toy MNIST and an aerial dataset. A second innovation claimed by the authors is adding Wavelet decomposition-based processing into the net. Unfortunately, this mostly only speeds up learning, as the ablation does not show meaningful improvements relative to the error bounds in later stages of training. Furthermore, the paper lacks a discussion of related work on incorporating wavelet ideas into neural networks. For example: -- Generic Deep Networks with Wavelet Scattering, by Ouyallon et al. -- Invariant scattering convolution networks, by Bruna et al and multiple more recent ones. Without either clear performance gains or more in-depth discussion of this novelty, it is not clear how to take it into account. When reading the paper, it appears that *boundary detection* for the cases that the authors are exploring is very directly related to 2-class semantic segmentation (road / non-road), the only difference being that the edge boundaries are weighted much higher in the cross-entropy loss. As such, there is a lot more recent net architecture work for semantic segmentation that should be directly applicable, and should perform much better than CaseNet when adapted to the task. As a result, the experiments and the significance of this paper are rather marginal. In experimental results, the authors threshold prediction with 0.5, which is suboptimal. The resulting metric, which is just *accuracy* is called incorrectly *average precision*. Instead, true definition of average precision should be used, that is not dependent on potentially suboptimal fixed thresholding but on the area under precision-recall curve instead. Finally, it would be helpful to do ablation and confidence bounds also on the main aerial road results, as the 15% gain is significantly more than the gain that appears in the toy dataset.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
High-Frequency guided Curriculum Learning for Class-specific Object Boundary Detection
This work addresses class-specific object boundary extraction, i.e., retrieving boundary pixels that belong to a class of objects in the given image. Although recent ConvNet-based approaches demonstrate impressive results, we notice that they produce several false-alarms and misdetections when used in real-world applications. We hypothesize that although boundary detection is simple at some pixels that are rooted in identifiable high-frequency locations, other pixels pose a higher level of difficulties, for instance, region pixels with an appearance similar to the boundaries; or boundary pixels with insignificant edge strengths. Therefore, the training process needs to account for different levels of learning complexity in different regions to overcome false alarms. In this work, we devise a curriculum-learning-based training process for object boundary detection. This multi-stage training process first trains the network at simpler pixels (with sufficient edge strengths) and then at harder pixels in the later stages of the curriculum. We also propose a novel system for object boundary detection that relies on a fully convolutional neural network (FCN) and wavelet decomposition of image frequencies. This system uses high-frequency bands from the wavelet pyramid and augments them to conv features from different layers of FCN. Our ablation studies with contourMNIST dataset, a simulated digit contours from MNIST, demonstrate that this explicit high-frequency augmentation helps the model to converge faster. Our model trained by the proposed curriculum scheme outperforms a state-of-the-art object boundary detection method by a significant margin on a challenging aerial image dataset.
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 main idea of the paper is adding a curriculum learning-based extension to CASEnet, a boundary detection method from 2017. In the first phase, the loss emphasizes easier examples with high gradient in the image, and in the second phase, the method is trained on all boundary pixels. This change seems to improve edge detection performance on a toy MNIST and an aerial dataset. A second innovation claimed by the authors is adding Wavelet decomposition-based processing into the net. Unfortunately, this mostly only speeds up learning, as the ablation does not show meaningful improvements relative to the error bounds in later stages of training. Furthermore, the paper lacks a discussion of related work on incorporating wavelet ideas into neural networks. For example: -- Generic Deep Networks with Wavelet Scattering, by Ouyallon et al. -- Invariant scattering convolution networks, by Bruna et al and multiple more recent ones. Without either clear performance gains or more in-depth discussion of this novelty, it is not clear how to take it into account. When reading the paper, it appears that *boundary detection* for the cases that the authors are exploring is very directly related to 2-class semantic segmentation (road / non-road), the only difference being that the edge boundaries are weighted much higher in the cross-entropy loss. As such, there is a lot more recent net architecture work for semantic segmentation that should be directly applicable, and should perform much better than CaseNet when adapted to the task. As a result, the experiments and the significance of this paper are rather marginal. In experimental results, the authors threshold prediction with 0.5, which is suboptimal. The resulting metric, which is just *accuracy* is called incorrectly *average precision*. Instead, true definition of average precision should be used, that is not dependent on potentially suboptimal fixed thresholding but on the area under precision-recall curve instead. Finally, it would be helpful to do ablation and confidence bounds also on the main aerial road results, as the 15% gain is significantly more than the gain that appears in the toy dataset.
assistant
The main idea of the paper is adding a curriculum learning-based extension to CASEnet, a boundary detection method from 2017. In the first phase, the loss emphasizes easier examples with high gradient in the image, and in the second phase, the method is trained on all boundary pixels. This change seems to improve edge detection performance on a toy MNIST and an aerial dataset. A second innovation claimed by the authors is adding Wavelet decomposition-based processing into the net. Unfortunately, this mostly only speeds up learning, as the ablation does not show meaningful improvements relative to the error bounds in later stages of training. Furthermore, the paper lacks a discussion of related work on incorporating wavelet ideas into neural networks. For example: -- Generic Deep Networks with Wavelet Scattering, by Ouyallon et al. -- Invariant scattering convolution networks, by Bruna et al and multiple more recent ones. Without either clear performance gains or more in-depth discussion of this novelty, it is not clear how to take it into account. When reading the paper, it appears that *boundary detection* for the cases that the authors are exploring is very directly related to 2-class semantic segmentation (road / non-road), the only difference being that the edge boundaries are weighted much higher in the cross-entropy loss. As such, there is a lot more recent net architecture work for semantic segmentation that should be directly applicable, and should perform much better than CaseNet when adapted to the task. As a result, the experiments and the significance of this paper are rather marginal. In experimental results, the authors threshold prediction with 0.5, which is suboptimal. The resulting metric, which is just *accuracy* is called incorrectly *average precision*. Instead, true definition of average precision should be used, that is not dependent on potentially suboptimal fixed thresholding but on the area under precision-recall curve instead. Finally, it would be helpful to do ablation and confidence bounds also on the main aerial road results, as the 15% gain is significantly more than the gain that appears in the toy dataset.