这样,Vision-RWKV继承了RWKV在处理全局信息和稀疏输入方面的效率,同时也能够建模视觉任务的局部概念。作者在需要的地方实施了层尺度和层归一化,以稳定模型在不同尺度下的输出。这些调整在模型扩大规模时显著提高了稳定性。 1 Vision-RWKV Overall Architecture 在本节中,作者提出了Vision-RWKV(VRWKV),这是一种具...
Vision-RWKV 支持稀疏输入和稳定的扩展,通过类似 ViT 的块叠加图像编码器设计,包括用于注意力和特征融合的空间混合和通道混合模块。VRWKV 通过将图像转换为补丁,添加位置嵌入来形成图像标记,然后通过 L 个相同的编码器层处理图像,保持输入分辨率。 视觉版本的 RWKV 修改了原始论文的注意力机制有三个关键变化: 引入...
We report the #Param and #FLOPs of the backbone in this table. Citation If this work is helpful for your research, please consider citing the following BibTeX entry. @article{duan2024vrwkv,title={Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures},author={Dua...
- 提出的框架优化了网络图像-文本对的数据质量 - 高效架构使得RWKV-CLIP在计算和内存方面更高效 - 代码和预训练模型已开源,促进未来研究 一篇基于RWKV的CLIP实验报告,主要改动在于 text augmentation,随机从raw, synthetic和generated三种方式中采样一个 backbone使用RWKV 一图胜千言 synthetic text可以理解为上一代...
记住最重要的一点,Encoder 仅处理可见(un-masked)的 patches。Encoder 本身可以是 ViT 或 ResNet(其它 backbone 也 ok,就等你去实现了,大神给了你机会),至于如何将图像划分成 patch 嘛,使用 ViT 时的套路是这样的: 先将图像从 (B,C,H,W) reshape 成 (B,N,PxPxC),其中 N 和 P 分别为 patch 数量 ...
HaloNet: "Scaling Local Self-Attention For Parameter Efficient Visual Backbones", CVPR, 2021 (Google). [Paper][PyTorch (lucidrains)] CoTNet: "Contextual Transformer Networks for Visual Recognition", CVPRW, 2021 (JD). [Paper][PyTorch] HAT-Net: "Vision Transformers with Hierarchical Attention", ...
Conditional Prompt Learning for Vision-Language Models Kaiyang Zhou Jingkang Yang Chen Change Loy Ziwei Liu S-Lab, Nanyang Technological University, Singapore {kaiyang.zhou, jingkang001, ccloy, ziwei.liu}@ntu.edu.sg Abstract With the rise of powerful pre-trained vision-language...
HaloNet: "Scaling Local Self-Attention For Parameter Efficient Visual Backbones", CVPR, 2021 (Google). [Paper][PyTorch (lucidrains)] CoTNet: "Contextual Transformer Networks for Visual Recognition", CVPRW, 2021 (JD). [Paper][PyTorch] HAT-Net: "Vision Transformers with Hierarchical Attention", ...
Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures; Yuchen Duan, Weiyun Wang, Zhe Chen, Xizhou Zhu, Lewei Lu, Tong Lu, Yu Qiao, Hongsheng Li, Jifeng Dai, Wenhai Wang (Paper, Code) MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection; Tia...
More model archs, incl a flexible ByobNet backbone ('Bring-your-own-blocks') GPU-Efficient-Networks (https://github.com/idstcv/GPU-Efficient-Networks), impl inbyobnet.py RepVGG (https://github.com/DingXiaoH/RepVGG), impl inbyobnet.py ...