具体来说,Inception网络会为输入的生成图像输出一个概率分布向量,表示图像属于ImageNet数据集中1000个类别的概率。然后,Inception Score通过对这些概率分布进行计算来评估生成图像的质量和多样性。 总结与发散 与CLIP是同期工作,CLIP是多模态latent特征对齐的方法,不能做图像生成,而本文是text-image的图像生成方法。
Zero-shot text-to-image generation[C]//International conference on machine learning. Pmlr, 2021: 8821-8831. 论文链接: [2102.12092] Zero-Shot Text-to-Image Generation (arxiv.org) 代码链接: github.com/openai/DALL- 简介 传统上,文本到图像生成侧重于为固定数据集上的训练找到更好的建模假设。这些...
Sample Generation,生成结果的时候,生成N个image结果,用一个预训练好的contrastive model(其实就是CLIP)判断text和image 匹配分数,选择分数最高的那个,论文中采用N=512 Results 效果很好,同时在MSCOCO上 zero-shot的表现也很好;CUB数据集上效果一般,可能在某些特定的分布上还是不能泛化 Thoughts 用codebook编码到latent...
ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. While such models can provide ... Y Tewel,Y Shalev,I Schwartz,... 被引量: 0发表: 2021年 CJ...
Zero-Shot Text-to-Image Generation A. Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, I. Sutskever 2021 CogView: Mastering Text-to-Image Generation via Transformers Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang...
Zero-Shot Text-to-Image Generation for Housing Floor Plans share.streamlit.io/vrmusketeers/deeplearningproject/main/streamlitapp/streamlit.py Resources Readme Activity Custom properties Stars 3 stars Watchers 1 watching Forks 2 forks Report repository Releases No releases published Packages...
Image credit: GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsBenchmarks Add a Result These leaderboards are used to track progress in Zero-Shot Text-to-Image Generation No evaluation results yet. Help compare methods by submitting evaluation metrics. ...
Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic - YoadTew/zero-shot-image-to-text
Recent advancements in zero-shot text-to-speech (TTS) modeling have led to significant strides in generating high-fidelity and diverse speech. However, dialogue generation, along with achieving human-like naturalness in speech, continues to be a challenge. In this paper, we introduce CoVoMix: ...
Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image. Previous methods have relied on domain-specific heuristics such as warping-based motion representation and 3D Morphable Models, which limit the naturalness and diversity of the ...