CVPR 2018学习笔记（2018-07-02更新）

workshop

1. Robust vision challenge 2018
   
   robust vision.JPG

会议视频

• 语义分割11个submissions，

第一名 Peter Samuel Rota Bulo (Mapillary): In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

pytorch code

key observation of 当前分割方法

我们关注的是减少memory consume in 训练，因为这可以让我们用larger crops, larger batch size or deeper models.

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结论

大数据集上预训练非常有用！！！

• In-PlaceABN 中文笔记

第二名 Marin Oršić (Uni Zagreb): Ladder-DenseNet Architecture for Robust Semantic Segmentation

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Instance segment 3 submissions.

• Shou-Yao Roy Tseng (NTHU): Non-local RoI for Instance Segmentation

• Hsien-Tzu Cheng (NTHU): Improved MaskRCNN

• Invited talk

  • Stefan Roth (TU Darmstadt): Robust Scene Analysis: Energy-based models, deep learning, and something in between
  • Uwe Franke (Daimler AG): 30 Years Fighting for Robustness
  • Judy Hoffman (UC Berkeley): Making our Models Robust to Changing Visual Environments

2. CV for microscopy image analysis

• Cell Image Segmentation by Integrating Multiple CNNs

• Comparison of deep transfer learning strategies for digital pathology

• Understanding Pixel-to-Label Prediction Model

• Invited Talk: Life and death decisions- classification, characterization and predictions of death in neuronal models of neurodegenerative disease (Jeremy Linsley (UCSF))

• 文章列表

3. Medical Computer Vision and Health Informatics Workshop (CVPR 2018)

• Deep Learning for Biomedical Imaging: Can We Get Better, Higher or Faster? Tammy Riklin Raviv, PhD
• Deep Lesion Database and Deep Lesion Graphs on Relationship Learning and Organization of Significant Radiology Image Findings Ke YAN's blog
• Mine Deeper & Learn Wider: a Perspective on Distilling Radiological Reports for Chest X-ray Analysis Wang Xiaosong

ChestX-ray8 数据贡献者

• Population imaging analytics: progress, challenges and opportunities

4. Embedded Vision
   论文列表

感兴趣文章

• MIT- Learning Network Architectures of Deep CNNs under Resource Constraints
  
• Recurrent Segmentation for Variable Computational Budgets
• Probabilistic model for 3D interactive segmentation

5. Tutorial: Interpreting and Explaining Deep Models in Computer Vision

Youtube视频

6. Interpretable Machine Learning for Computer Vision

MIT 周博磊等人组织