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Meta AI SHOCKS The Industry And Take The Lead Again With ImageBind A Way To LINK AI Across Senses

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FurkanGozukara edited this page Oct 25, 2025 · 1 revision

Meta AI SHOCKS The Industry And Take The Lead Again With ImageBind: A Way To LINK AI Across Senses

Full tutorial link > https://www.youtube.com/watch?v=IMLIXfLMjSk

Meta AI SHOCKS The Industry And Take The Lead Again With ImageBind: A Way To LINK AI Across Senses

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Introducing ImageBind, a revolutionary AI model capable of binding information from six modalities including text, image/video, audio, depth (3D), thermal (infrared radiation), and inertial measurement units (IMU). This open-source model aims to mimic humans' ability to learn holistically from diverse forms of information without explicit supervision.

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Official link ⤵️

https://ai.facebook.com/blog/imagebind-six-modalities-binding-ai/

GitHub link ⤵️

https://github.com/facebookresearch/ImageBind

Interactive Demo link ⤵️

https://imagebind.metademolab.com/demo?modality=I2A

Research paper PDF link ⤵️

https://dl.fbaipublicfiles.com/imagebind/imagebind_final.pdf

00:00:00 Introducing to new groundbreaking ImageBind

00:00:14 What is ImageBind?

00:00:52 Interactive demo of ImageBind

00:02:46 Official demo video of Meta ImageBind

00:03:30 Official research paper supplementary video of ImageBind

#science #imagebind #meta

The research paper presents IMAGEBIND, a novel approach that learns a joint embedding from six different modalities - images, text, audio, depth, thermal, and Inertial Measurement Unit (IMU) data. The primary innovation of IMAGEBIND is its ability to create this joint embedding using only image-paired data, leveraging the 'binding' property of images. This approach effectively extends the zero-shot capabilities of large-scale vision-language models to other modalities by merely using their natural pairing with images.

The introduction of the paper underscores the idea that a single image can bind together a multitude of sensory experiences. However, acquiring all types and combinations of paired data with the same set of images is challenging. Previous methods have attempted to learn image features aligned with text, audio, and other modalities, but these final embeddings have been limited to the pairs of modalities used for training, and therefore, cannot be utilized universally. IMAGEBIND overcomes this problem by aligning each modality's embedding to image embeddings, leading to an emergent alignment across all modalities.

IMAGEBIND uses web-scale (image, text) paired data and combines it with naturally occurring paired data such as (video, audio), (image, depth), etc., to learn a single joint embedding space. This setup enables the alignment of text embeddings with other modalities such as audio and depth, thus enabling zero-shot recognition capabilities without explicit semantic or textual pairing. The paper further explains that IMAGEBIND can be initialized with large-scale vision-language models like CLIP, which offers the advantage of using the rich image and text representations of these models. This makes IMAGEBIND highly versatile, applicable to a variety of different modalities and tasks with minimal training.

The authors demonstrate the effectiveness of IMAGEBIND by using large-scale image-text paired data along with naturally paired 'self-supervised' data across four new modalities - audio, depth, thermal, and IMU readings. They report strong emergent zero-shot classification and retrieval performance on tasks for each of these modalities, with improvements as the underlying image representation is made stronger. On audio classification and retrieval benchmarks, IMAGEBIND's emergent zero-shot classification matches or even outperforms specialist models trained with direct audio-text supervision on benchmarks like ESC, Clotho, AudioCaps.

Additionally, IMAGEBIND's representations also outperform specialist supervised models on few-shot evaluation benchmarks. The paper concludes by demonstrating the wide range of applications for IMAGEBIND's joint embeddings. These include cross-modal retrieval, combining embeddings via arithmetic, detecting audio sources in images, and generating images given audio input. Thus, IMAGEBIND sets a new standard in emergent zero-shot recognition tasks across modalities, and it also provides a new way to evaluate vision models for visual and non-visual tasks.

Video Transcription

  • 00:00:00 Greetings everyone.

  • 00:00:01 Meta AI just recently published their new industry shocking AI model ImageBind and this

  • 00:00:07 is something new.

  • 00:00:08 Believe me, I have never seen something like this and I will explain everything about it

  • 00:00:13 in this video to you.

  • 00:00:14 So what is ImageBind?

  • 00:00:17 ImageBind provides a holistic understanding allowing machines to comprehend the connection

  • 00:00:21 between objects in an image, their sounds, 3D shapes, temperature, and movement.

  • 00:00:27 ImageBind could be potentially used in applications like composing images from audio, moderating

  • 00:00:33 content, enhancing productive design, and multimodal research functions.

  • 00:00:38 ImageBind is different from typical artificial intelligence systems that require a specific

  • 00:00:43 embedding for each modality.

  • 00:00:45 Instead, it composes a joint embedding space across multiple modalities without the need

  • 00:00:51 to train on data from every combination of modalities.

  • 00:00:54 They released an amazing demo page where you can interactively play and see.

  • 00:01:00 For example, when you select an image, it will automatically bring audio for them.

  • 00:01:06 Let's listen the sound for this bird.

  • 00:01:08 When I click the images, you see the sound changes.

  • 00:01:13 You see it is amazing.

  • 00:01:15 I will put the link of this page, announcement page, and the GitHub page in the description

  • 00:01:20 of the video.

  • 00:01:21 Let's check out an example from audio to image.

  • 00:01:24 You see when I click a dog barking, it brings me these images.

  • 00:01:28 Let's listen that audio.

  • 00:01:32 So from this audio, the model brings these images.

  • 00:01:35 Let's see text to image and audio.

  • 00:01:38 And when I click trains, from using this trains text, it brings these images and their relative

  • 00:01:44 sounds.

  • 00:01:45 Let's listen them.

  • 00:01:50 Just amazing.

  • 00:01:51 Let's see audio and image to image.

  • 00:01:53 So by combining apple images and the pouring sound, it will bring me a new image.

  • 00:01:59 When I click it, you see this is the image it brings.

  • 00:02:02 Let's also listen its sound.

  • 00:02:04 Just amazing.

  • 00:02:07 It is able to combine the sound and the image and produce a new image like this.

  • 00:02:12 Let's also see the audio to generated image.

  • 00:02:15 Now I will listen rain sound first.

  • 00:02:19 And when I click it, it is bringing me this image from this sound.

  • 00:02:24 And now let's watch their supplementary video and

  • 00:02:33 their released demo video.

  • 00:02:46 It works more like our own imagination.

  • 00:02:48 If you give it a picture of a beach, it can find the sound of waves.

  • 00:02:56 If you give it a photo of a tiger and the sound of a waterfall, it can give you a video

  • 00:03:03 that combines both.

  • 00:03:05 This is a step towards AIs that understand the world around them more like we do, which

  • 00:03:14 will make them a lot more useful and will open up totally new ways to create things.

  • 00:03:19 We're open sourcing ImageBind.

  • 00:03:23 So everyone in the world can access and build on top of these state of the art models.

  • 00:03:27 I'm excited to see what you build.

  • 00:03:38 Woof, woof, woof, woof, woof, woof, woof, woof, woof.

  • 00:05:08 Woof, woof, woof, woof, woof.

  • 00:05:58 Woof, woof, woof, woof, woof, woof, woof.

  • 00:07:08 Thank you very much for watching.

  • 00:07:10 I hope you have enjoyed this video.

  • 00:07:12 Please like, subscribe, leave a comment.

  • 00:07:14 If you support me by joining our YouTube channel or support me on Patreon, I would appreciate

  • 00:07:19 that very much.

  • 00:07:20 Hopefully very soon an amazing deep voice cloning video is coming.

  • 00:07:24 I am working on it.

  • 00:07:26 It is almost ready.

  • 00:07:27 I also have amazing tutorials for large language models or Stable Diffusion or other generative

  • 00:07:34 AI, AI related things.

  • 00:07:36 Hopefully see you in another amazing video.

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