Meta AI Introduces IMAGEBIND: The First Open Source AI Project Capable Of Binding Data From Six Modalities At Once, Without Needing Explicit Supervision

Human beings can grasp complex ideas after being exposed to just a few cases. Most of the time, we can identify an animal based on a written description and guess the sound of an unknown car engine based on an image. This is partly because a single image can “stitch together” a different sensory experience. Based on paired data, standard multimodal learning has limitations in AI as the number of modalities increases.

Aligning text, audio, etc., with images has been the focus of several recent methodologies. These strategies only make use of two senses at most, if at all. The final embeddings, however, can only represent the training modalities and their corresponding pairs. For this reason, it is not possible to directly transfer video and audio embeds to image and text activities or vice versa. The lack of large amounts of multimodal data where all modalities are present together is a major barrier to learning true joint integration.

New Meta research introduces IMAGEBIND, a system that uses various forms of image pair data to learn a single shared representation space. It is not necessary to use data sets in which all modalities occur simultaneously. Instead, this paper takes advantage of the binding property of images and demonstrates how aligning the embedding of each modality with the embeddings of images results in emergent alignment across all modalities.

The sheer number of images and accompanying text on the web has led to substantial research on image and text model training. ImageBind takes advantage of the fact that images often coexist with other modalities and can serve as a bridge to connect them, such as linking text to image with online data or linking motion to video with video data acquired from handheld cameras with IMU sensors.

The targets for learning features in all modalities may be the visual representations learned from massive amounts of web data. This means that ImageBind can also align any other modality that frequently appears next to images. Alignment is easier for modalities such as heat and depth that correlate highly with images.

ImageBind demonstrates that the simple use of paired images can integrate all six modalities. The model can provide a more holistic interpretation of information by allowing the various modalities to “talk” to each other and discover connections without direct observation. For example, ImageBind can link sound and text even if you can’t see them together. By doing so, other models can “get the hang of” new modalities without requiring extensive training that takes a lot of time and energy. ImageBind’s robust scaling behavior makes it possible to use the model instead of or in addition to many AI models that previously could not use additional modalities.

The strong emerging zero-shot classification and recovery performance in tasks for each new modality is demonstrated by combining large-scale paired image and text data with naturally paired self-monitored data in four new modalities: audio, depth, thermal, and inertial measurement unit (IMU) readings. The team shows that strengthening the underlying image rendering improves these emergent features.

The findings suggest that IMAGEBIND’s emergent zero-shot classification in audio classification and retrieval benchmarks such as ESC, Cloto, and AudioCaps is on par with or exceeds expert models trained with direct audio-text supervision. In low-shot evaluation benchmarks, IMAGEBIND renderings also perform better than expert-supervised models. Finally, they demonstrate the versatility of IMAGEBIND joint embeddings in various compositing tasks, including multimodal retrieval, arithmetic combination of embeddings, detection of audio sources in images, and generation of images from audio input.

Since these embeds are not trained for a particular application, they lag behind domain-specific models in efficiency. The team believes it would be useful to learn more about how to tailor general-purpose embeds to specific goals, such as structured prediction tasks like detection.

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