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modality encoders

A modality encoder is a specialized neural network component in multimodal artificial intelligence designed to process and transform raw data from a specific input format, such as text, images, audio, depth, or sensor measurements, into dense mathematical representations known as embeddings. These encoders extract semantic and structural features unique to their designated data type, mapping high-dimensional inputs into standardized vector spaces. In multimodal architectures, multiple modality-specific encoders frequently project their outputs into a shared or aligned representation space, enabling distinct data types to be directly compared, combined, and translated for tasks including cross-modal retrieval, zero-shot classification, and multimodal generation.

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ImageBind One Embedding Space to Bind Them All

ImageBind One Embedding Space to Bind Them All

Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra

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Why you should read this

Demonstrates that aligning various modalities (audio, depth, thermal) to images automatically aligns them to each other, creating a universal embedding space.

Part of:Must-read papers in multimodal AI

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 can leverage recent large scale vision-language models, and extends their zero-shot capabilities to new modalities just by using their natural pairing with images. It enables novel emergent applications 'out-of-the-box' including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation. The emergent capabilities improve with the strength of the image encoder and we set a new state-of-the-art on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models. Finally, we show strong few-shot recognition results outperforming prior work, and that ImageBind serves as a new way to evaluate vision models for visual and non-visual tasks.

Added

2026-01-28