Effects of Network Depths on Semantic Image Segmentation By Weakly Supervised Learning

Bircanoglu, Cenk; Arıca, Nafiz

doi:10.1109/siu49456.2020.9302471

Cited by 1 publication

(3 citation statements)

References 9 publications

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“…Self-supervised equivariant attention mechanism (SEAM) [20], embedded discriminative attention mechanism (EDAM) [21], and image segmentation with iterative masking (ISIM) [22] are methods used for self-improvement in computer vision. SEAM uses a Siamese network that takes both original and augmented images as input to produce a CAM (class activation map) at the output.…”

Section: Weakly Supervised Methodsmentioning

confidence: 99%

“…To refine CAM results, the proposed pixelregion correlation module (PRCM) is used. This module finds semantic relations between regions or pixels and uses information with the help of the PCM module proposed in the ISIM work [22]. In the PPL [24] method, the image is split into patches.…”

Section: Weakly Supervised Methodsmentioning

confidence: 99%

“…Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation [34] Image-level labels 68.0 ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation [22] Image-level labels 70.38…”

Section: Few-shots Methodsmentioning

confidence: 99%

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Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations

et al. 2023

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Plant segmentation is a challenging computer vision task due to plant images complexity. For many practical problems, we have to solve even more difficult tasks. We need to distinguish plant parts rather than the whole plant. The major complication of multi-part segmentation is the absence of well-annotated datasets. It is very time-consuming and expensive to annotate datasets manually on the object parts level. In this article, we propose to use weakly supervised learning for pseudo-annotation. The goal is to train a plant part segmentation model using only bounding boxes instead of fine-grained masks. We review the existing weakly supervised learning approaches and propose an efficient pipeline for agricultural domains. It is designed to resolve tight object overlappings. Our pipeline beats the baseline solution by 23% for the plant part case and by 40% for the whole plant case. Furthermore, we apply instance-level augmentation to boost model performance. The idea of this approach is to obtain a weak segmentation mask and use it for cropping objects from original images and pasting them to new backgrounds during model training. This method provides us a 55% increase in mAP compared with the baseline on object part and a 72% increase on the whole plant segmentation tasks.

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Section: Weakly Supervised Methodsmentioning

confidence: 99%

Section: Weakly Supervised Methodsmentioning

confidence: 99%