[2]OpenMask3D: Open-vocabulary 3d instance segmentation. NeurIPS2023 最后多说一句,我突然发现我写的这点东西竟然有人在看,真是感谢读者给我反馈,写这个知乎的本意是强迫自己进行输出,可以参考 @靖焙态 提到的“转入为出”。没什么特殊情况的话,我每天都会强迫自己读2篇已中稿的顶会论文,并对其中一篇进行...
This approach yet simple, achieves state-of-the-art performance across a wide range of 3D open-vocabulary tasks, including recognition, object detection, and instance segmentation, on both indoor and outdoor datasets. Moreover, OpenIns3D facilitates effortless switching between different 2D detectors ...
《Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling》(CVPR 2022) GitHub: github.com/hbdat/cvpr22_cross_modal_pseudo_labeling [fig5]《GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation》(CVPR 2022) GitHub: github.com/microsoft/GRAM...
instance-segmentation open-vocabulary-segmentation 3d-understanding Updated Oct 1, 2023 Jupyter Notebook Improve this page Add a description, image, and links to the open-vocabulary-segmentation topic page so that developers can more easily learn about it. Curate this topic Add this topic to...
speech image-editing caption data-generation 3d-whole-body-pose-estimation open-vocabulary-detection open-vocabulary-segmentation automatic-labeling-system Updated Sep 5, 2024 Jupyter Notebook roboflow / notebooks Star 5.5k Code Issues Pull requests Discussions Examples and tutorials on using SOTA ...
Additionally, we explore several applications of Semantic Gaussians including object part segmentation, instance segmentation, scene editing, and spatiotemporal segmentation with better qualitative results over 2D and 3D baselines, highlighting its versatility and effectiveness on supporting diverse downstream ...
Similarly, X-Decoder [45] adopts a joint training approach for segmentation and image-text pairs, harnessing OV-S capabilities for downstream tasks. Furthermore, open vocabulary video comprehension and open vocabulary 3D comprehension have seen significant advancements in recent times [46], [47]. ...
A universal framework FreeSeg is proposed to employ an all-in- one model with the same architecture and inference parameters to accomplish open-vocabulary semantic, instance, and panoptic segmentation. • Adaptive prompt learning explicitly encodes multi- granularity concepts ...
POMP achieves APr of 26.8 for object detection and 25.2 for instance segmentation. See Appendix D for qualitative results. Under the cross-dataset setting, we pre-train the visual backbone on the source dataset of standard LVIS, and evaluate the recognition ability on COCO and Object365. As ...
Paper329 MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation 本文介绍了MosaicFusion,这是一种独特的基于扩散的数据增强方法,可以在不需要任何训练或标签监督的情况下优化大型词汇实例分割。该系统使用现成的文本到图像扩散模型来创建对象实例和掩膜注释的数据集。这种方法将图像...