16 Best Multimodal Models for Enhancing AI's Creative Potential

In today’s world of artificial intelligence, researchers are constantly looking for ways to develop more efficient models that can solve real-world problems. Multimodal models, which analyze data across multiple modalities, such as text, images, and audio, pave the way for more intelligent AI that can mimic human-like understanding by processing and correlating information across different data types. Suppose you want to achieve faster, smarter, and more efficient AI development that drives innovation and real-world impact, particularly in creative applications. In that case, this article will offer valuable insights into the best multimodal models for machine learning frameworks currently available.

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What are Multimodal Models?

Multimodal models are advanced AI systems that leverage deep learning to process and integrate multiple data modalities simultaneously, such as:

• Text
• Audio
• Video
• Images

These models enable a more context-rich and accurate understanding by combining information from diverse sources. Unlike unimodal models, which rely on traditional machine learning to process a single data type at a time (e.g., YOLO for visual data), multimodal frameworks deliver higher accuracy and an improved user experience.

Their versatility makes them valuable across industries, from autonomous mobile robots in manufacturing that fuse sensor data for object localization to healthcare applications that combine medical imaging and patient records for more precise diagnoses.

How Multimodal Models Work

Although multimodal models have varied architectures, most frameworks have a few standard components. A typical architecture includes:

• An Encoder
• A fusion mechanism
• A decoder

Encoders Transform Raw Multimodal Data Into Machine-Readable Inputs

Encoders transform raw multimodal data into machine-readable feature vectors or embeddings that models use as input to understand the data’s content. Multimodal models often have three types of encoders for each data type:

• Image
• Text
• Audio

Modality-Specific Encoders in Multimodal AI Systems

• Image Encoders: Convolutional neural networks (CNNs) are popular for image encoders. CNNs can convert image pixels into feature vectors to help the model understand critical image properties.
• Text Encoders: Text encoders transform text descriptions into embeddings that models can use for further processing. They often use transformer models like those in Generative Pre-Trained Transformer (GPT) frameworks.
• Audio Encoders: Wav2Vec2 is a popular choice for learning audio representations. Audio encoders convert raw audio files into usable feature vectors that capture critical audio patterns, including:
  • Rhythm
  • Tone
  • Context

Fusion Mechanisms Combine Multiple Input Modalities

Once the encoders transform multiple modalities into embeddings, the next step is to combine them so the model can understand the broader context reflected in all data types. Developers can use various fusion strategies according to the use case.

The list below mentions key fusion strategies.

• Early Fusion: Combines all modalities before passing them to the model for processing.
• Intermediate Fusion: Projects each modality onto a latent space and fuses the latent representations for further processing.
• Late Fusion: Processes all modalities in their raw form and fuses the output for each.
• Hybrid Fusion: Combines early, intermediate, and late fusion strategies at different model processing phases.

Methods of Fusing Modalities

While the list above mentions the high-level fusion strategies, developers can use multiple methods within each strategy to fuse the relevant modalities.

Attention-based Methods

Attention-based methods, built on the transformer architecture, convert embeddings from multiple modalities into a query-key-value format for context-aware processing. Introduced in the landmark 2017 paper “Attention Is All You Need,” this technique was initially developed to enhance language models by enabling longer context windows. Today, attention mechanisms are widely applied across domains such as computer vision and generative AI.

These methods allow models to capture relationships between different embeddings, facilitating more accurate interpretation of multimodal data. Cross-modal attention enables models to align and integrate inputs from various modalities. For example, identifying which text prompt elements correspond to specific visual features in an image, resulting in more effective data fusion.

Concatenation

Concatenation is a straightforward fusion technique that merges multiple embeddings into a single feature representation. For instance, the method will concatenate a textual embedding with a visual feature vector to generate a consolidated multimodal feature. The technique helps in intermediate fusion strategies by combining the latent representations for each modality.

Dot-Product

The dot-product method involves element-wise multiplication of feature vectors from different modalities. It helps capture the interactions and correlations between modalities, assisting models in understanding the commonalities among different data types. It only helps in cases where the feature vectors do not suffer from high dimensionality.

Taking dot-products of high-dimensional vectors may require extensive computational power and result in features that only capture common patterns between modalities, disregarding critical nuances.

Decoders Generate Output From Combined Features

The last component is a decoder network that processes the feature vectors from different modalities to produce the required output. Decoders can contain cross-modal attention networks to focus on other parts of the input data and produce relevant outputs.

For instance, translation models often use cross-attention techniques to simultaneously understand the meanings of sentences in different languages. Recurrent neural network (RNN), Convolutional Neural Networks (CNN), and Generative Adversarial Network (GAN) frameworks are popular choices for constructing decoders to perform tasks involving:

• Sequential
• Visual
• Generative processes

Multimodal Models - Use Cases

With recent advancements in multimodal models, AI systems can perform complex tasks involving the simultaneous integration and interpretation of multiple modalities. The capabilities allow users to implement AI in large-scale environments with extensive and diverse data sources requiring robust processing pipelines.

The list below mentions a few of these tasks that multimodal models perform efficiently.

Visual Question-Answering (VQA)

VQA involves a model answering user queries regarding visual content. For instance, a healthcare professional may ask a multimodal model regarding the content of an X-ray scan. By combining visual and textual prompts, multimodal models provide relevant and accurate responses to help users perform VQA.

Image-to-Text and Text-to-Image Search

Multimodal models help users build powerful search engines that can type natural language queries to search for particular images. They can also build systems that retrieve relevant documents in response to image-based queries. For instance, a user may give an image as input to prompt the system to search for relevant blogs and articles containing the image.

Generative AI

Generative AI models help users with text and image generation tasks that require multimodal capabilities. For instance, multimodal models can help users with image captioning, where they ask the model to generate relevant labels for a particular image. They can also use these models for natural language processing (NLP) use cases that involve generating textual descriptions based on:

• Video
• Image
• Audio data

Image Segmentation

Image segmentation involves dividing an image into regions to distinguish between different elements within an image. Multimodal models can help users perform segmentation more quickly by segmenting areas automatically based on textual prompts. For instance, users can ask the model to segment and label items in the image’s background.

Related Reading

• Natural Language Processing Techniques

16 Best Multimodal Models for Advanced AI Development

1. Inference

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2. Llama 3.2 90B

Meta AI’s Llama 3.2 90B is one of the most advanced and popular multimodal models. This latest variant of the Llama series combines instruction-following capabilities with advanced image interpretation, catering to a wide range of user needs.

The model is built to facilitate tasks requiring understanding and generating responses based on multimodal inputs.

Features

• Instruction Following: Designed to handle complex user instructions involving text and images.
• High Efficiency: Capable of processing large datasets quickly, enhancing its utility in dynamic environments.
• Robust Multimodal Interaction: Integrates text and visual data to provide comprehensive responses.

3. Gemini 1.5 Flash

Gemini 1.5 Flash is Google’s latest lightweight multimodal model. It is adept at processing text, images, video, and audio quickly and efficiently. Its ability to provide comprehensive insights across different data formats makes it suitable for applications that require a deeper understanding of context.

Features

• Multimedia Processing: Handles multiple data types simultaneously, allowing for enriched interactions.
• Conversational Intelligence: Particularly effective in multi-turn dialogues, context from previous interactions is vital.
• Dynamic Response Generation: Generates responses that reflect an understanding of various media inputs.

4. Florence 2

Florence 2 is a lightweight model from Microsoft designed primarily for computer vision tasks while integrating textual inputs. Its capabilities enable it to perform complex analyses on visual content, making it invaluable for vision-language applications such as:

• OCR
• Captioning
• Object detection
• Instance segmentation, etc.

Features

• Strong Visual Recognition: Excels at identifying and categorizing visual content, providing detailed insights.
• Complex Query Processing: Handles user queries that combine both text and images effectively.

5. GPT-4o

GPT-4o is an optimized version of GPT-4, designed for efficiency and performance in processing both text and images. Its architecture allows quick responses and high-quality outputs, making it a preferred choice for various applications.

Features

• Optimized Performance: Faster processing speeds without sacrificing output quality, suitable for real-time applications.
• Multimodal Capabilities: Effectively handles various queries involving textual and visual data.

6. Claude 3.5

Claude 3.5 is a multimodal model developed by Anthropic, focusing on ethical AI and safe interactions. This model combines text and image processing while prioritizing user safety and satisfaction.

It is available in three sizes:

• Haiku
• Sonnet
• Opus

Features

• Safety Protocols: Designed to minimize harmful outputs, ensuring that interactions remain constructive.
• Human-Like Interaction Quality: Emphasizes creating natural, engaging responses, making it suitable for a broad audience.
• Multimodal Understanding: Effectively integrates text and images to provide comprehensive answers.

7. LLaVA V1.5 7B

LLaVA (Large Language and Vision Assistant) is a fine-tuned model. It uses visual instruction tuning to support image-based natural instruction following and visual reasoning capabilities. Its small size makes it suitable for interactive applications, such as chatbots or virtual assistants, requiring real-time user engagement.

Its strengths lie simultaneously in processing:

• Text
• Audio
• Images

Features

• Real-Time Interaction: Provides immediate responses to user queries, making conversations more natural.
• Contextual Awareness: Better understanding of user intents that combine various data types.
• Visual Question Answering: Identifies text in images through Optical Character Recognition (OCR) and answers questions based on image content.

8. DALL·E 3

Open AI’s DALL·E 3 is a powerful image generation model that translates textual descriptions into vivid, detailed images. This model is renowned for its creativity and ability to understand nuanced prompts, enabling users to generate images that closely match their imagination.

Features

• Text-to-Image Generation: Converts detailed prompts into unique images, allowing for extensive creative possibilities.
• Inpainting Functionality: Users can modify existing images by describing text changes, offering flexibility in image editing.
• Advanced Language Comprehension: It better understands context and subtleties in language, resulting in more accurate visual representations.

9. CLIP

Contrastive Language-Image Pre-training (CLIP) is a multimodal vision-language model by OpenAI that performs image classification tasks. It pairs descriptions from textual datasets with corresponding images to generate relevant image labels.

Key Features

• Contrastive Framework: CLIP uses the contrastive loss function to optimize its learning objective. The approach minimizes a distance function by associating relevant text descriptions with related images to help the model understand which text best describes an image’s content.
• Text and Image Encoders: The architecture uses a transformer-based text encoder and a Vision Transformer (ViT) as an image encoder.
• Zero-shot Capability: Once CLIP learns to associate text with images, it can quickly generalize to new data and generate relevant captions for new unseen images without task-specific fine-tuning.

10. CogVLM

Cognitive Visual Language Model (CogVLM) is an open-source visual language foundation model that uses deep fusion techniques to achieve superior vision and language understanding. The model performs SOTA on seventeen cross-modal benchmarks, including image captioning and VQA datasets.

Key Features

• Attention-based Fusion: The model uses a visual expert module that includes attention layers to fuse text and image embeddings. This technique helps retain the LLM’s performance by keeping its layers frozen.
• ViT Encoder: It uses EVA2-CLIP-E as the visual encoder and a multi-layer perceptron (MLP) adapter to map visual features onto the same space as text features.
• Pre-trained Large Language Model (LLM): CogVLM 17B uses Vicuna 1.5-7B as the LLM for transforming textual features into word embeddings.

11. Gen2

Gen2 is a powerful text-to-video and image-to-video model that can generate realistic videos based on textual and visual prompts. It uses diffusion-based models to create context-aware videos using image and text samples as guides.

Key Features

• Encoder: Gen2 uses an autoencoder to map input video frames onto a latent space and diffuse them into low-dimensional vectors.
• Structure and Content: It uses MiDaS, an ML model that estimates the depth of input video frames. It also uses CLIP for image representations by encoding video frames to understand content.
• Cross-Attention: The model uses a cross-modal attention mechanism to merge the diffused vector with the content and structure representations derived from MiDaS and CLIP. It then performs the reverse diffusion process conditioned on content and structure to generate videos.

12. ImageBind

ImageBind is a multimodal model by Meta AI that can combine data from six modalities, including text, video, audio, depth, thermal, and inertial measurement unit (IMU), into a single embedding space. It can then use any modality as input to generate output in any of the mentioned modalities.

Key Features

• Output: ImageBind generates corresponding images by supporting
  • Audio-to-image
  • Image-to-audio
  • Text-to-image and audio
  • Audio and image-to-image
  • Audio
• Image Binding: The model pairs image data with other modalities to train the network. For instance, it finds relevant textual descriptions related to specific images and pairs videos from the web with similar images.
• Optimization Loss: It uses the InfoNCE loss, where NCE stands for noise-contrastive estimation. The loss function uses contrastive approaches to align non-image modalities with specific images.

13. Flamingo

Flamingo is a vision-language model by DeepMind that can take videos, images, and text as input and generate textual responses regarding the photo or video. The model allows for few-shot learning, where users provide a few samples to prompt the model to create relevant responses.

Key Features

• Encoders: The model consists of a frozen pre-trained Normalizer-Free ResNet as the vision encoder trained on the contrastive objective. The encoder transforms image and video pixels into 1-dimensional feature vectors.
• Perceiver Resampler: The perceiver resampler generates a small number of visual tokens for every image and video. This method helps reduce computational complexity in photos and videos with an extensive feature set.
• Cross-Attention Layers: Flamingo incorporates cross-attention layers between the layers of the frozen LLM to fuse visual and textual features.

14. Gemini

Google Gemini is a set of multimodal models that can process audio, video, text, and image data.

It offers Gemini in three variants:

• Ultra for complex tasks,
• Pro for large-scale deployment
• Nano for on-device implementation

Key Features

• Larger Context Window: The latest Gemini versions, 1.5 Pro and 1.5 Flash, have long context windows, which allow them to process long-form videos, text, code, and words. For instance, Gemini 1.5 Pro supports up to two million tokens, and 1.5 Flash supports up to one million.
• Transformer-based Architecture: Google trained the model on interleaved text, image, video, and audio sequences using a transformer. Using the multimodal input, the model generates images and text as output.
• Post-training: The model uses supervised fine-tuning and reinforcement learning with human feedback (RLHF) to improve response quality and safety.

15. Aria

The recently introduced Aria AI model from Rhymes AI is touted as the world’s first open source, multimodal native mixture-of-experts (MoE) model, all within one architecture, that can process:

• Text
• Code
• Images
• Video

This versatile model is relatively powerful compared to even larger models, yet is more efficient, as it selectively leverages relevant subsets (or “mini-experts”) of its framework, depending on the task. Its architecture is designed for ease of scalability, as new “experts” could be added to address new tasks without straining the system. Aria excels at long multimodal input understanding, meaning it quickly and accurately parses long documents and videos.

16. xGen-MM

BLIP-3, developed by Salesforce, is a cutting-edge, open-source suite of multimodal models featuring several variants:

• A base pretrained model
• An instruction-tuned version
• A safety-tuned model designed to minimize harmful outputs

A significant advancement lies in its training approach, leveraging a massive, trillion-token open-source dataset of interleaved image and text data, which researchers describe as the most natural form of multimodal input. This enables BLIP-3 to effectively process inputs combining text with multiple images, making it highly adaptable across domains such as:

• Autonomous driving
• Medical diagnostics
• Interactive education
• Marketing

Key Highlights

1. Multiple Model VariantsIncludes base, instruction-tuned, and safety-tuned options for varied use cases.
2. Safety-Tuned DesignAims to reduce the risk of generating harmful or inappropriate outputs.
3. Trillion-Token Multimodal DatasetTrained on interleaved image-text data, optimizing real-world applicability.
4. Enhanced Multimodal UnderstandingExcels at interpreting and reasoning over combined text and multi-image inputs.
5. Wide-Ranging ApplicationsSuitable for use in healthcare, autonomous systems, education, and marketing.

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