整理：AI算法与图像处理
CVPR2022论文和代码整理：https://github.com/DWCTOD/CVPR2022-Papers-with-Code-Demo
ECCV2022论文和代码整理：https://github.com/DWCTOD/ECCV2022-Papers-with-Code-Demo
最新成果demo展示：
标题：ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision
主页：https://gerwang.github.io/shadowneus/
摘要：
通过监督场景和多视图图像平面之间的相机光线，NeRF 为新视图合成任务重建神经场景表示。另一方面，光源和场景之间的阴影光线还有待考虑。因此，我们提出了一种新颖的阴影射线监督方案，可以优化沿射线的样本和射线位置。通过监督阴影光线，我们在多种光照条件下成功地从单视图纯阴影或 RGB 图像重建场景的神经 SDF。
最新论文整理
ECCV2022
Updated on : 29 Nov 2022
total number : 2
Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classifcation
- 论文/Paper: http://arxiv.org/pdf/2211.14559
- 代码/Code: None
CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors
- 论文/Paper: http://arxiv.org/pdf/2211.14557
- 代码/Code: None
CVPR2022
NeurIPS
Updated on : 29 Nov 2022
total number : 10
Hand-Object Interaction Image Generation
- 论文/Paper: http://arxiv.org/pdf/2211.15663
- 代码/Code: None
Investigating Prompt Engineering in Diffusion Models
- 论文/Paper: http://arxiv.org/pdf/2211.15462
- 代码/Code: None
Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity
- 论文/Paper: http://arxiv.org/pdf/2211.15436
- 代码/Code: None
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
- 论文/Paper: http://arxiv.org/pdf/2211.15071
- 代码/Code: None
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
- 论文/Paper: http://arxiv.org/pdf/2211.15059
- 代码/Code: https://github.com/rehg-lab/dope_selfsup
Performance evaluation of deep segmentation models on Landsat-8 imagery
- 论文/Paper: http://arxiv.org/pdf/2211.14851
- 代码/Code: None
3D Reconstruction of Protein Complex Structures Using Synthesized Multi-View AFM Images
- 论文/Paper: http://arxiv.org/pdf/2211.14662
- 代码/Code: None
Unsupervised Wildfire Change Detection based on Contrastive Learning
- 论文/Paper: http://arxiv.org/pdf/2211.14654
- 代码/Code: None
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data
- 论文/Paper: http://arxiv.org/pdf/2211.14694
- 代码/Code: https://github.com/AilsaF/DigGAN
Where to Pay Attention in Sparse Training for Feature Selection?
- 论文/Paper: http://arxiv.org/pdf/2211.14627
- 代码/Code: None
