2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) 2020
DOI: 10.1109/sibgrapi51738.2020.00036
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IDA: Improved Data Augmentation Applied to Salient Object Detection

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Cited by 9 publications
(5 citation statements)
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“…For the parameters of the AR images in fog condition, the transparency of the weather layer was adjusted. According to KMA's weather condition definition (22), heavy fog allows visibility less than 40 m, and it was expected to be difficult to distinguish the effect of fog on the images when the distance between the VPDS camera and vehicles is less than 5 m. There was a limitation that the difference was distinguished visually by setting the transparency lower than the expected value in consideration of the case where the distance between the VPDS and vehicles is far enough to show the fog effect.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…For the parameters of the AR images in fog condition, the transparency of the weather layer was adjusted. According to KMA's weather condition definition (22), heavy fog allows visibility less than 40 m, and it was expected to be difficult to distinguish the effect of fog on the images when the distance between the VPDS camera and vehicles is less than 5 m. There was a limitation that the difference was distinguished visually by setting the transparency lower than the expected value in consideration of the case where the distance between the VPDS and vehicles is far enough to show the fog effect.…”
Section: Discussionmentioning
confidence: 99%
“…In the augmentation process, the original label information is converted into its corresponding AR image without re-labeling. Previous studies indicate that the performance of the CNN algorithm was improved through augmentation and was helpful for generalization (20)(21)(22). To improve the performance of a large number of image classification tasks such as ImageNet ( 23), performance has been increased using simple methods such as image translations, horizontal reflections, and changing red, green, and blue (RGB) pixel values.…”
Section: Generation Of the Ar Imagesmentioning
confidence: 99%
“…To verify the effectiveness of our data augmentation method, five methods are compared with ours. These methods are without data augmentation, H-Flip [14], ANDA [49], IDA [7], and GridMask [31]. In addition to the data augmentation method changes, the other parts of the network are unchanged.…”
Section: Compared With Recent Data Augmentation Methodsmentioning
confidence: 99%
“…These methods can achieve better image classification results, but cannot obtain pixel‐wise virtual knowledge. The self‐distillation method based on data augmentation [7] can increase the number and diversity of training samples, but may lose the local information between samples due to different distortion and rotation. The above problems will inevitably lead to the failure of object segmentation.…”
Section: Introductionmentioning
confidence: 99%
“…They showed that this strategy increases the number of anchor boxes generated by the Mask-RCNN [He et al 2017], which helps the network to learn and detect small objects. The ANDA [Ruiz et al 2019] and IDA [Ruiz et al 2020a] techniques follow the idea of introducing new objects, however since those are techniques focused on the generic problem of Salient Object Detection (SOD), some additional operations are necessary such as Image Inpainting to erase the original object and some additional computation to choose which combination of background and object produce a significant salience and the affine transformations to be applied to the new object that will replace the original one.…”
Section: Related Workmentioning
confidence: 99%