与MaskFormer结构类似,先通过一个transformer decoder输出segment-level embeddings,接着分别通过mask projection(用于class-agnostic grouping简称CAG)和semantic projection(用于segment-level zero-shot classification简称s-ZSC)。除了semantic proj
通过N个 queries 得到 N 个 Segment embedding。 Segment embedding 再分别通过 Mask projection 和 Semantic projection 就能得到 Mask embedding和Semantic segment embedding。 N个 Mask embedding 与 dxHxW 特征图相乘即可得到 N 个 无类别 mask N个 Semantic segment embedding 与文本生成的 Text embedding 做余弦相...
Teams:Technical University of Munich;IMT School for Advanced Studies Lucca;Lund University; Writers:Virmarie Maquiling,Sean Anthony Byrne,ADiederick C. Niehorster,Marcus Nyström,Enkelejda Kasneci PDF:Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM) Abstract The advent o...
multi-label contrastive loss with text prompting涉及到较为复杂的操作,可以参考原文进一步了解: 第三步:通过设计好的GroupViT结构,模型能够自动将image分组成一个个的segment,所以可以很容易的zero-shot transfer到语义分割任务上,而不需要微调。由于GroupViT自动将图像分组为语义相似的片段,它的输出可以很容易地转移到...
Zero-shot learning 就是希望我们的模型能够对其从没见过的类别进行分类,让机器具有推理能力,实现真正的智能。其中零次(Zero-shot)是指对于要分类的类别对象,一次也不学习。 1.2 实例 假设我们的模型已经能够识别马、老虎和熊猫了,现在需要该模型也识别斑马,那么我们需要告诉模型,怎样的对象才是斑马,但是并不能直接让...
This is a proof of concept for zero-shot panoptic segmentation using theSegment Anything Model (SAM). SAM cannot immediately achieve panoptic segmentation due to two limitations: The released version of SAM is not text-aware The authors of Segment Anything mention that it is unclear how to desig...
Segment Anything project是一个用于图像分割的新任务、模型和数据集。在他刚出来的那一天,知乎等平台就已经高呼CV已死。为了这个项目,作者创建了迄今为止最大的分割数据集,1100万张在10亿次授权且尊重隐私的图像上的数据集。模型也被设计和训练成了promptable,就是说可以给他一些提示。作者在多个数据集测试了他的结...
论文题目:Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion / DiffSeg 论文地址:http://arxiv.org/abs/2308.12469 * 视频受up能力限制经常出现中英混杂,散装英语等现象,请见谅。如论文理解报道出了偏差,欢迎各位怒斥。 ** 新论文推荐,过往论文查找,欢迎编辑这个文档: https...
We propose Segment Any Mesh (SAMesh), a novel zero-shot method for mesh part segmentation that overcomes the limitations of shape analysis-based, learning-based, and current zero-shot approaches. SAMesh operates in two phases: multimodal rendering and 2D-to-3D lifting. In the first phase, mu...
The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g.,...