OMG-Seg: Is One Model Good Enough For All Segmentation? 单位:南洋理工大学, 上海AI Lab 主页:lxtgh.github.io/project 代码:github.com/lxtGH/OMG-Se 论文:arxiv.org/abs/2401.1022 CVPR 2024 论文和开源项目合集请戳—>github.com/amusi/CVPR20 本文解决了各种分割任务,每个任务传统上都是通过不同或部分...
1. 论文信息 标题:OMG-Seg: Is One Model Good Enough For All Segmentation? 作者:Xiangtai Li, Haobo Yuan, Wei Li, Henghui Ding, Size Wu, Wenwei Zhang, Yining Li, Kai Chen, Chen Change Loy 机构:南阳理工大学S-Lab、上海AI Lab 原文链接:https://arxiv.org/abs/2401.10229 代码链接:https://g...
参考 [1] OMG-Seg : Is One Model Good Enough For All Segmentation? 投稿作者为『自动驾驶之心知识星球』特邀嘉宾,欢迎加入交流! 自动驾驶技术与行业发展日常分享,专注自动驾驶与AI 51篇原创内容 ① 全网独家视频课程 BEV感知、毫米波雷达视觉融合、多传感器标定、多传感器融合、多模态3D目标检测、车道线检测、轨...
参考 [1] OMG-Seg : Is One Model Good Enough For All Segmentation? 写在最后 欢迎star和follow我们的仓库,里面包含了BEV/多模态融合/Occupancy/毫米波雷达视觉感知/车道线检测/3D感知/目标跟踪/多模态/多传感器融合/Transformer/在线高精地图/高精地图/SLAM/多传感器标定/Nerf/视觉语言模型/世界模型/规划控制/...
CVPR'24《OMG-Seg : Is One Model Good Enough For All Segmentation?》 多模态大语言模型MLLMs 仅具有图像级能力的MLLMs 具有目标级能力的MLLMs 具有像素级能力的MLLMs 具有目标级和像素级能力但系统非常复杂的MLLMs OMG-LLaVA的...
[CV] OMG-Seg: Is One Model Good Enough For All Segmentation? O网页链接 介绍了一种名为OMG-Seg的模型,能统一处理各种分割任务,包括图像语义分割、实例分割、全景分割、视频分割等。这是首个能在一个模型中处理这些任务并取得令人满意性能的模型。OMG-Seg采用了基于Transformer的编-解码器架构,并通过任务特定...
Test Interactive COCO segmentation: ./tools/dist.sh test seg/configs/m2ov_val/eval_m2_convl_ov_coco_pan_point.py 4 --checkpoint model_path Test Youtube-VIS-19 dataset ./tools/dist.sh test seg/configs/m2ov_val/eval_m2_convl_300q_ov_y19.py 4 --checkpoint model_path ...
We present OMG-LLaVA, a new and elegant framework combining powerful pixel-level vision understanding with reasoning abilities. It can accept various visual and text prompts for flexible user interaction. Specifically, we use a universal segmentation method as the visual encoder, integrating image info...
IMAGE segmentationMYASTHENIA gravisDIAGNOSISMEDICAL care costsPHYSICIANSThis paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic...
special_tokens = segmentation_tokens + phrase_tokens + region_tokens self.tokenizer.add_tokens(special_tokens, special_tokens=True) self.seg_token_idx = self.tokenizer("[SEG]", add_special_tokens=False).input_ids[0] self.bop_token_idx = self.tokenizer("", add_special_tokens=False)...