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：SCAM! Transferring humans between images with Semantic Cross Attention Modulation
主页：https://imagine.enpc.fr/~dufourn/publications/scam.html
代码：https://github.com/nicolas-dufour/SCAM
论文：https://arxiv.org/pdf/2210.04883v1.pdf
最近的大量工作以语义条件下的图像生成为目标。大多数这类方法只关注较窄的姿势转移任务，而忽略了更具挑战性的对象转移任务，即不仅转移姿势，还转移外观和背景。在这项工作中，我们引入了SCAM（Semantic Cross Attention Modulation，语义交叉注意调制），这是一个系统，它对图像的每个语义区域（包括前景和背景）中丰富多样的信息进行编码，从而实现了以细节为重点的精确生成。这是由Semantic Attention Transformer Encoder实现的，该编码器为每个语义区域提取多个潜在向量，以及通过使用语义交叉注意调制来利用这些潜在向量的相应生成器。它仅使用重建设置进行训练，而受试者在测试时进行转移。我们的分析表明，我们提出的架构在编码每个语义区域的外观多样性方面是成功的。iDesigner和CelebAMask HD数据集上的大量实验表明，SCAM优于SEAN和SPADE；此外，它还开创了学科转移的新境界。
最新论文整理
ECCV2022
Updated on : 14 Oct 2022
total number : 1
Improving the Reliability for Confidence Estimation
- 论文/Paper: http://arxiv.org/pdf/2210.06776
- 代码/Code: None
CVPR2022
NeurIPS
Updated on : 14 Oct 2022
total number : 17
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
- 论文/Paper: http://arxiv.org/pdf/2210.07242
- 代码/Code: https://github.com/Jingkang50/OpenOOD
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
- 论文/Paper: http://arxiv.org/pdf/2210.07158
- 代码/Code: None
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
- 论文/Paper: http://arxiv.org/pdf/2210.07124
- 代码/Code: PaddleSeg:https://github.com/PaddlePaddle/PaddleSeg.
LION: Latent Point Diffusion Models for 3D Shape Generation
- 论文/Paper: http://arxiv.org/pdf/2210.06978
- 代码/Code: None
SageMix: Saliency-Guided Mixup for Point Clouds
- 论文/Paper: http://arxiv.org/pdf/2210.06944
- 代码/Code: https://github.com/mlvlab/SageMix
Feature-Proxy Transformer for Few-Shot Segmentation
- 论文/Paper: http://arxiv.org/pdf/2210.06908
- 代码/Code: https://github.com/Jarvis73/FPTrans
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
- 论文/Paper: http://arxiv.org/pdf/2210.06871
- 代码/Code: None
Scalable Neural Video Representations with Learnable Positional Features
- 论文/Paper: http://arxiv.org/pdf/2210.06823
- 代码/Code: None
ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation
- 论文/Paper: http://arxiv.org/pdf/2210.06816
- 代码/Code: None
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation
- 论文/Paper: http://arxiv.org/pdf/2210.06780
- 代码/Code: https://github.com/LIUYUANWEI98/IPMT
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer
- 论文/Paper: http://arxiv.org/pdf/2210.06707
- 代码/Code: onhttps://github.com/YanjingLi0202/Q-ViT
Structural Pruning via Latency-Saliency Knapsack
- 论文/Paper: http://arxiv.org/pdf/2210.06659
- 代码/Code: None
S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spaces
- 论文/Paper: http://arxiv.org/pdf/2210.06583
- 代码/Code: None
Task-Free Continual Learning via Online Discrepancy Distance Learning
- 论文/Paper: http://arxiv.org/pdf/2210.06579
- 代码/Code: None
Flare7K: A Phenomenological Nighttime Flare Removal Dataset
- 论文/Paper: http://arxiv.org/pdf/2210.06570
- 代码/Code: None
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
- 论文/Paper: http://arxiv.org/pdf/2210.07182
- 代码/Code: https://github.com/pdebench/PDEBench.
Brain Network Transformer
- 论文/Paper: http://arxiv.org/pdf/2210.06681
- 代码/Code: https://github.com/Wayfear/BrainNetworkTransformer.
