风格迁移
[1] CLIPstyler: Image Style Transfer with a Single Text Condition(具有单一文本条件的图像风格迁移)
医学影像
[2] Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations(时间上下文很重要:使用疾病进展表示增强单图像预测)
图像去模糊
[3] E-CIR: Event-Enhanced Continuous Intensity Recovery(事件增强的连续强度恢复)
代码:https://github.com/chensong1995/E-CIR
图像生成/图像合成
[4] 3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces(基于小批量特征交换的三维形状变化自动编码器潜在解纠缠
代码:https://github.com/simofoti/3DVAE-SwapDisentangled
[5] Interactive Image Synthesis with Panoptic Layout Generation(具有全景布局生成的交互式图像合成)
[6] Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values(极性采样:通过奇异值对预训练生成网络的质量和多样性控制)
demo:http://bit.ly/polarity-demo-colab
[7] Autoregressive Image Generation using Residual Quantization(使用残差量化的自回归图像生成
代码:https://github.com/kakaobrain/rq-vae-transformer
3D目标检测
[8] A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation(在全景分割的指导下,用于基于 LiDAR 的 3D 对象检测的多功能多视图框架
[9] Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving(自动驾驶中用于单目 3D 目标检测的伪立体)
代码:https://github.com/revisitq/Pseudo-Stereo-3D
深度估计
[10] ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks(立体匹配网络中自动避免捷径和域泛化的信息论方法)
[11] ACVNet: Attention Concatenation Volume for Accurate and Efficient Stereo Matching(用于精确和高效立体匹配的注意力连接体积)
代码:https://github.com/gangweiX/ACVNet
超分辨率
[12] HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging(光谱压缩成像的高分辨率双域学习)
车道线检测
[13] Rethinking Efficient Lane Detection via Curve Modeling(通过曲线建模重新思考高效车道检测)
代码:https://github.com/voldemortX/pytorch-auto-drive
人脸反欺诈
[14] Voice-Face Homogeneity Tells Deepfake
代码:https://github.com/xaCheng1996/VFD
半监督学习
[15] Class-Aware Contrastive Semi-Supervised Learning(类感知对比半监督学习
图像重建
[16] Event-based Video Reconstruction via Potential-assisted Spiking Neural Network(通过电位辅助尖峰神经网络进行基于事件的视频重建)
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