Open-set semantic segmentationGeneric segmentationScene understandingIn this paper, we extend Open-set Semantic Segmentation (OSS) into a new image segmentation task called Generalized Open-set Semantic Segmentation (GOSS). Previously, with well-known OSS, the intelligent agents only detect unknown ...
Open-set semantic segmentation remains yet a challenging task, not only due to the inherent challenges of pixel-wise classification but also the precise se... I Nunes,C Laranjeira,HSJA Oliveira - 《Computers & Graphics》 被引量: 0发表: 2023年 On advantages of Mask-level Recognition for Open...
A Prototypical Metric Learning Approach for Open-Set Semantic Segmentation on Remote Sensing Images Semantic segmentation has received wide attention as a feasible solution to effectively interpret the information in remote sensing images. Solutions are t......
In semantic segmentation, we aim to train a pixel-level classifier to assign category labels to all pixels in an image, where labeled training images and unlabeled test images are from the same distribution and share the same label set. However, in an open world, the unlabeled test images ...
《Open-World Semantic Segmentation Including Class Similarity》(CVPR 2024) GitHub: github.com/PRBonn/ContMAV《Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting》(CVPR 2024) GitHub: github.com/sharinka0715/semantic-gaussians...
Table 6: Hyper-parameter tuning for open-set semantic segmentation on Cityscapes. Given a fixed number of open training images, we vary the hyper-parameter λG to train OpenGAN models. Recall that λG controls the contribution of synthesized data in the loss function. We conduct model ...
the easier it is to find the unknown objects. Semantic segmentation is a more challenging task than classification because the algorithm has both to understand the representative information of the different pixels and to learn the interrelationships to build a clear object boundary. Reference26aggregate...
Note that the provided checkpoint for semantic map is only trained on ADE20K dataset; the checkpoint for normal map is only trained on DIODE dataset. Inference: Generate images with GLIGEN We provide one script to generate images using provided checkpoints. First download models and put them ing...
Deep Metric Learning for Open World Semantic Segmentation. Jun Cen, Peng Yun, Junhao Cai, Michael Yu Wang, Ming Liu. (ICCV 2021) NGC: A Unified Framework for Learning With Open-World Noisy Data. Zhi-Fan Wu, Tong Wei, Jianwen Jiang, Chaojie Mao, Mingqian Tang, Yu-Feng Li. (ICCV 2021...
2023/07/18: We releaseSemantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity.Codeandcheckpointare available! 2023/06/17: We provide an example to evaluate Grounding DINO on COCO zero-shot performance. ...