Existing GAN-based generative methods are typically used for semantic image synthesis. We pose the question of whether GAN-based architectures can generate
Face reshaping aims to adjust the shape of a face in a portrait image to make the face aesthetically beautiful, which has many potential applications. Exis
* 题目: X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360$^{\circ} $ Insufficient RGB-D Views* PDF: arxiv.org/abs/2210.0513* 作者: Haoyi Zhu,Hao-Shu Fang,Cewu Lu* 相关: github.com/HaoyiZhu/XNe* 题目: NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using...
关于RGB,RGB-D,RGB-T和视频SOD基准的广泛实验证明,我们的框架可以针对现有的USOD方法实现最新性能,并且与最新的监督SOD方法相当。* Domain Gap Estimation for Source Free Unsupervised Domain Adaptation with Many Classifiers* 链接: arxiv.org/abs/2207.0578* 作者: Ziyang Zong,Jun He,Lei Zhang,Hai Huan* ...
RGB image [7–9,13] and 3D scene [1,3,11,12,14,15] are the commonest forms and carriers of visual information. Image generation tasks always synthesize the pixel matrices from semantic features. StackGAN [7] and StackGAN++ [8] generate images from text descriptions. They are composed of...
Machine learning for aerial image labeling[D]. University of Toronto (Canada), 2013.Comments: poor quality of dataset.ISPRS-Vaihingen/Potsdam dataset 6 urban land cover classes, raster mask labels, 4-band RGB-IR aerial imagery (0.05m res.) & DSM, 38 image patches. ISPRS Potsdam 2D Semantic...
Experimental results show that the proposed framework outperforms the H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics across a wide range of bit rates in RGB domain. Besides, since semantic segmentation map is included in the bit-stream, the proposed scheme can ...
The render network R, which is only conditioned on the feature map, refines upsampled m into a high-resolution segmentation mask by learning a residual ∆m and generates the fake image. A dual-branch discriminator models the joint distribution of...
2.5 d convolution for rgb-d semantic segmentation. In: 2019 IEEE international conference on image processing (ICIP). IEEE; 2019. p. 1410–14. Zhao Z-Q, Zheng P, Xu S-T, Wu X. Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst. 2019;30(11):3212–32...
Hou, J., Dai, A., Nießner, M.: RevealNet: seeing behind objects in RGB-D scans. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020) Google Scholar Huan, L., Zheng, X., Gong, J.: GeoRec: geometry-enhanced semantic 3D reconstruction of RGB-D indoor scene...