Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identically applying them in multiple feature stages.
Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation
Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information.
GitHub Link
The GitHub link is https://github.com/mvme-hbut/sgacnetIntroduce
Title GitHub Repository Update for SGACNet Spatial-information Guided Adaptive Context-aware Network. Summary This GitHub repository branch is current and synced with the CyunXiong/SGACNetmain repository, which pertains to the development of SGACNet—a network designed for efficient RGB-D semantic segmentation, incorporating spatial information guidance and adaptive context awareness. Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information.Content
We provide the weights for our selected ESANet-R34-NBt1D (with ResNet34 NBt1D backbones) on NYUv2, SunRGBD, and Cityscapes. Download and extract the models to ./trained_models. Please navigate to the cloned directory. Note we are using Python 3.7+. Torch 1.3.1 and torchvision 0.4.2 ImageNet can be downloaded for our selected backbones on the above datasets. Stored in <dir>/trained_models/imagenet. Note that some parameters are different in Cityscapes. Evaluation on SUN RGB-D is similar to NYUv2. Yang Zhang, Chenyun Xiong, Junjie Liu, Xuhui Ye, and Guodong Sun. Spatial-information Guided Adaptive Context-aware Network for Efficient RGBD Semantic Segmentation[J]. IEEE Sensors Journal, 2023.
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