不开启人脸超分没问题,请教这是哪里出了问题? 应该是gfpgan 相关的包没有安装好,请检查下以下是否都安装了 pip install facexlib pip install tb-nightly -i https://mirrors.aliyun.com/pypi/simple pip install gfpgan 同时也推荐原本的https://github.com/TencentARC/GFPGAN 看看他的安装教程 Author theold...
machine-learningdeep-learningartificial-intelligencefaceface-recognitionface-detectionface-landmarkface-expressionface-clusteringface-manipulationface-anti-spoofingface-3dface-benchmarkface-actionface-ganface-deblurringface-super-resolutionface-paperface-codeawesome-face ...
1、题目 《FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning》 作者: 2、创新点 * The effectiveness of existing face super-resolution approaches is not known when they are applied to low resolution face image with occlusions。 * It is not known whether the face comple...
Face Super-Resolution && Face Deblurring && Face Hallucination; Face Generation && Face Synthesis && Face Completion && Face Restoration && Face De-Occlusion; Face Transfer && Face Editing && Face swapping; Face Anti-Spoofing; Face Retrieval; Face Application; DataSets also, some papers and links ...
我们将继续发展ONNX，PyTorch和Caffe2，确保开发人员拥有AI的最新工具。敬请期待后续更新！相关资源 ONNX：https://github.com/onnx Caffe2：https://caffe2.ai/ PyTorch：http://pytorch.org/ Super Resolution Tutorial：http://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html ...
The high temporal resolution of the iEEG signal enabled us to investigate whether the maximally correlated DCNN layers were altered throughout the neural response. To this end, we computed the maximally correlated DCNN layer at a sliding window of 200 ms, with a 50 ms stride. The results...
Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19 Sensors, 20 (18) (2020), p. 5236 CrossrefGoogle Scholar [20] S. Ioffe, C. Szegedy Batch normalization: accelerating deep network training by reducing internal covariate shift Proceedin...
super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) ...
@InProceedings{feng2024keep, title = {Kalman-Inspired FEaturE Propagation for Video Face Super-Resolution}, author = {Feng, Ruicheng and Li, Chongyi and Loy, Chen Change}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2024} }...
Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network (ACMMM 2024) Paper (Arxiv)|Supplementary Material|Project Page Installation and Requirements I have trained and tested the codes on Ubuntu 20.04 CUDA 11.1 Python 3.8, install required packages bypip install -r requirements....