- ImageBind: One Embedding Space To Bind Them All - GitHub
ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation
- ImageBind: One Embedding Space To Bind Them All
We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together
- ImageBind by Meta AI
ImageBind: a new way to ‘link’ AI across the senses Introducing ImageBind, the first AI model capable of binding data from six modalities at once, without the need for explicit supervision
- 多模态超详细解读 (十一)：ImageBind：图像配对数据绑定6种模态
因此，ImageBind 可以在只进行少量训练的情况下轻松应用于多种模态任务。 ImageBind 使用的数据集不仅有图像-文本对，还包括了4种新的模态：audio, depth, thermal, 和 Inertial Measurement Unit (IMU)，并在每种模态的任务上面都表现出了强大的 Emergent Zero-Shot 分类和检索性能。
- ImageBind: One Embedding Space To Bind Them All
ImageBind 将所有这些模态连接在一个共同的嵌入空间中，从而实现了新的涌现对齐和能力。 本文的核心目标是利用图像作为中心枢纽，将所有模态绑定在同一个联合嵌入空间中。
- ImageBind革命性突破：多模态融合的新范式，一文读懂核心原理-CSDN博客
ImageBind的革命性在于其"One Embedding Space To Bind Them All"的设计理念。 不同于传统多模态模型需要两两配对训练（如图像- 文本 、音频-文本），ImageBind创新性地以图像作为"中枢模态"，通过图像与其他各模态的配对数据，将所有模态统一到同一个嵌入空间。
- nielsr imagebind-huge · Hugging Face
ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation
- ImageBind: One Embedding Space To Bind Them All - AI at Meta
We present IMAGEBIND, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data We show
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