In this paper, we propose a novel method to infer plate regions of food images without any pixel-wise annotation. We synthesize plate segmentation masks using difference of visualization in food image classifiers. To ...
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ISBN:
(纸本)9781728113319
In this paper, we propose a novel method to infer plate regions of food images without any pixel-wise annotation. We synthesize plate segmentation masks using difference of visualization in food image classifiers. To be concrete, we use two types of classifiers: a food category classifier and a food/nonfood classifier. Using the Class Activation Mapping (CAM) which is one of the basic visualization techniques of CNNs, a food category classifier can highlight food regions containing no plate regions, while a food/non-food category classifier can highlight food regions including plate regions. By taking advantage of the difference between the food regions estimated by visualization of two kinds of the classifiers, in this paper, we demonstrate that we can estimate plate regions without any pixel-wise annotation, and we proposed the approach for boosting the accuracy of weakly-supervised food segmentation using the plate segmentation. In experiments, we show the effectiveness of the proposed approach by evaluating and comparing the accuracy of the weakly-supervisedsegmentation. The proposed approaches certainly improved an image-level weakly-supervisedsegmentation method in the food domain and outperformed a well-known bounding box-level weakly-supervisedsegmentation method.
Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as...
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Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.
This article summarizes the corresponding half-day tutorial at ACM Multimedia 2018. This tutorial reviews recent progresses for pixel-level understanding with structured deep learning, including 1) human-centric analy...
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ISBN:
(纸本)9781450356657
This article summarizes the corresponding half-day tutorial at ACM Multimedia 2018. This tutorial reviews recent progresses for pixel-level understanding with structured deep learning, including 1) human-centric analysis: human parsing and pose estimation;2) part-based analysis: object part and face parsing;3) weakly-supervised analysis: object localization and semanticsegmentation;4) depth estimation: stereo matching.
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