RethNet: Object-by-Object Learning for Detecting Facial Skin Problems

  title={RethNet: Object-by-Object Learning for Detecting Facial Skin Problems},
  author={Shohrukh Bekmirzaev and Seoyoung Oh and Sangwook Yoo},
  journal={2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
Semantic segmentation is a hot topic in computer vision where the most challenging tasks of object detection and recognition have been handling by the success of semantic segmentation approaches. We propose a concept of objectby-object learning technique to detect 11 types of facial skin lesions using semantic segmentation methods. Detecting individual skin lesion in a dense group is a challenging task, because of ambiguities in the appearance of the visual data. We observe that there exist co… 


TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context
A new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently, which gives competitive and visually pleasing results for objects that are highly textured, highly structured, and even articulated.
Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF).
The Role of Context for Object Detection and Semantic Segmentation in the Wild
A novel deformable part-based model is proposed, which exploits both local context around each candidate detection as well as global context at the level of the scene, which significantly helps in detecting objects at all scales.
The Pascal Visual Object Classes (VOC) Challenge
The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models.
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
This work shows how to improve semantic segmentation through the use of contextual information, specifically, ' patch-patch' context between image regions, and 'patch-background' context, and formulate Conditional Random Fields with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.
Rethinking Atrous Convolution for Semantic Image Segmentation
The proposed `DeepLabv3' system significantly improves over the previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.
The Pascal Visual Object Classes Challenge: A Retrospective
A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
DenseASPP for Semantic Segmentation in Street Scenes
Densely connected Atrous Spatial Pyramid Pooling (DenseASPP) is proposed, which connects a set of atrous convolutional layers in a dense way, such that it generates multi-scale features that not only cover a larger scale range, but also cover that scale range densely, without significantly increasing the model size.