An exciting frontier in cognitive artificial intelligence involves building systems that integrate multiple modalities and synthesize meaning from language, images, video, audio, and structured knowledge sources such as relationship graphs. Adaptive applications such as conversational AI, search for video and images using language; autonomous robots and drones; and multimodal AI assistants will require systems that can interact with the world using all available modalities and respond appropriately in specific contexts.
Multimodal AI is a new paradigm of artificial intelligence in which different types of data, such as images, text, speech, and numerical data, are combined with multiple intelligence processing algorithms to achieve higher performance. Multimodal AI often outperforms single-modal AI in many real-world problems. Multimodal AI uses different data modalities, leading to a better understanding and analysis of information. A multimodal AI framework provides complex data fusion algorithms and machine learning technologies.
Multimodal systems with access to sensory and linguistic modes of intelligence process information the way humans do. Traditionally, AI systems are unimodal because they are designed to perform specific tasks, such as image processing and speech recognition. Systems receive a single sample of training data, from which they can identify matching pictures or words. The progress of artificial intelligence depends on its ability to process multimodal signals simultaneously, just like humans.
AI Multimodal Learning Systems:
Disjointed data are combined into a single model using multimodal learning components. Multimodal learning provides more dynamic predictions than a unimodal system processing different data sets since numerous senses are employed to view the same data, leading to more creative discoveries. For AI to advance, the capacity to handle multimodal data continuously is essential. To address the challenges of multimodal learning, Artificial Intelligence researchers have recently made exciting breakthroughs toward multimodal learning, such as:
DALL.E: It is an AI program developed by OpenAI that creates digital images from text descriptions.
Flava: This is a multimodal model trained by Meta over images and 35 different languages.
NUWA: Trained on images, videos, and text, this model can predict the next video frame and fill in incomplete images when given a text prompt or sketch.
Mural: It’s a digital workspace for visual collaboration and helps everyone on the team visualize together, unlock new ideas and solve challenging problems.
Align: This is an artificial intelligence model trained by Google over a noisy dataset of many image-text pairs.
Clip: This is a multi-modal artificial intelligence system developed by OpenAI to successfully perform a wide range of visual recognition tasks.
Florence: Released by Microsoft Research and capable of modeling space, time, and modality.
Applications of Multi-Model Artificial Intelligence:
Multimodal AI systems have various applications in various industries, including supporting advanced robotic assistants, augmenting advanced driver assistance systems and monitoring systems, and extracting business intelligence through context-driven data mining. Recent developments in multimodal artificial intelligence have given rise to many applications across modalities. They are:
Image Caption Generation: It is the process of recognizing the context of an image and annotating it with relevant labels using deep learning and computer vision.
Generating text to image: The task is to create an image conditional on the input text.
Visual Question Solving: This is a dataset containing open-ended questions about pictures.
Text-to-Image and Image-to-Text Search: The search engine identifies resources based on multiple modalities.
Text-to-speech: It is the artificial production of human voices. It can automatically translate text into spoken language.
Speech-to-Text Transcription: It deals with the recognition of spoken language and its conversion into text format
Deep learning (DL) solutions have recently exceeded human baselines in various natural language processing (NLP) and computer vision benchmarks, including SuperGLUE, GLUE, and SQuAD (e.g., ImageNet). Advances in individual modalities are evidence of perceptual or recognition capabilities achieved by highly efficient statistical mappings obtained using neural networks.
These simple tasks were considered extremely difficult to solve a decade ago, but today they are the main burden of AI in data centers, clients, and edge products. However, many insights that could be obtained through automated methods remain untapped in a multimodal environment.
Current tasks and architectures of multimodal artificial intelligence
By early 2022, multimodal AI systems will be experimenting with driving text/NLP and vision into an aligned input space to facilitate multimodal decision-making. Several tasks require a model to have at least some multimodal capacity. The following briefly overviews the four predominant workloads and corresponding SotA models.
Generate Image Description, Generate Text On Image
The most well-known models for text-to-image creation and creating image captions are OpenAI CLIP, DALL-E, and GLIDE, which are the successor to these models.
CLIP learns to anticipate which prints in a dataset are matched with certain descriptions by pre-training independent picture and text encoders. It’s interesting to note that CLIP possesses multimodal neurons that fire when exposed to both the classifier label text and the accompanying image, indicating a fused multimodal representation, similar to the “Halle Berry” neuron in humans. The DALL-E form of GPT-3, which has 13 billion parameters, uses text as input and produces a set of images that match the text as output. The resulting photos are then sorted using the CLIP function. GLIDE is an evolution of DALL-E that still uses CLIP to evaluate generated images; however, image generation is done using a diffusion model.
Visual Answer To The Question
Visual question answering, as presented in datasets such as VQA, is a task that requires a model to correctly answer an image-based text question. Microsoft Research teams have developed some of the leading approaches for this task. METER is a general framework for training powerful end-to-end vision language transformers using various possible sub-architectures for vision encoder, text encoder, multimodal fusion, and decoder modules. The Unified Vision-Language pretrained Model (VLMo) uses a modular transformer network to train a dual encoder and a fusion encoder jointly. Each block in the grid contains a pool of modality-specific experts and a shared self-observation layer that offers considerable flexibility for fine-tuning.
Text-To-Image And Image-To-Text Search
Web search is another important application of multimodal learning. An example dataset representing this task is WebQA, a multi-modal and multi-hop benchmark that simulates web search. WebQA was created by teams from Microsoft and Carnegie Mellon University.
In this task, the model must identify resources (either image or text) that can help answer the query. The model must consider more than one source for most questions to arrive at the correct answer. The system then needs to reason with these multiple sources to generate a natural language response to the query.
Google tackled the task of multimodal search using A Large-scale ImaGe and Noisy-Text Embedding Model (ALIGN). This model uses readily available but noisy alternative text data associated with images on the Internet to train separate visual (EfficientNet-L2) and textual (BERT-Large) encoders, whose outputs are combined using contrast learning. The resulting model stores multimodal representations that enable crossmodal search without further fine-tuning.
Video-Language Modeling
Due to their resource requirements, video-based activities have historically been difficult for AI systems to complete; however, this is beginning to change. Microsoft’s Florence-VL project is one of the major initiatives in the field of video-language modeling and other multimodal video-related tasks. In the middle of 2021, the Florence-VL project released ClipBERT, which combines CNN with a transformer model that operates on poorly sampled images and is end-to-end optimized to address common jobs in the video language. The evolutions of ClipBERT known as VIOLET and SwinBERT add Masked Visual-Token Modeling and Sparse Attention to enhance SotA in video search, question answering, and captioning.
Although there are some differences in the specifics, all of the models mentioned above use a transformer-based design. This architecture often combines parallel learning modules to extract data from different modalities and unify them into a single multimodal representation.
Conclusion
Environments in the real world are always multimodal. This application area enables the AI research community to progress the shift from the statistical analysis of a single perceptual modality (such as images or text) to a multifaceted perspective of things and their interaction, assisting in the progression from “form” to “meaning.”
