The rise of models like GPT-4o by OpenAI and Gemini by Google have made multimodal models more and more commonplace, and subsequently made multimodal data labeling fundamental to the AI development process. 

Multimodal models capture and process different data modalities, becoming the bridge to AI that understands and interacts with the real world. A critical question that developers and consumers of multimodal models should be asking themselves (and aren’t) is: “How do we ensure these multimodal models are aligned with human preferences while still being performant?” The answer: multimodal data labeling.

Multimodal models leverage different data modalities and use them to make predictions. In this article we will dive deeper into the process of multimodal data labeling, the technologies used, and the contribution of multimodal data labeling to AI advancements. 

Understanding data modalities 

Understanding the concept of data modalities is a prerequisite for understanding multimodal data labeling. Modality refers to the various data types that a system can combine and process for particular tasks. Some of the common data modalities are:

• Image: This modality contains visual data in the form of photographs, sketches, paintings, and drawings. 
• Text: Text modality includes documents written in natural language.
• Video: This modality combines visual and auditory data, mostly moving images accompanied by sounds. 
• Audio: Audio modality comprises sound data in the form of spoken words or music. 
• Sensor Data: This modality consists of data collected from sensors like GPs or environment sensors. 

To achieve multimodal models, we simply combine a subset or all of the listed modalities above during training. 

Unlike traditional foundation models focused on singular modalities, multimodal models integrate different modalities to ensure we capture all the perspectives of the problem at hand. For instance, if we were to develop a patient diagnosis system, a multimodal model would be the best fit than a unimodal one focusing on only one data source, like text data from the patient's record. We can capture and use text data from the records, image data from X-rays, audio data from stethoscopes, and sensor data from wearables like smartwatches. This model would have a more holistic view of the patient's health than if we trained only on a particular modality. 

So how can we efficiently combine data from diverse sources in a way that can be used to train a single model? The answer is proper multimodal data labeling that supports cross-modal relationship building. 

Tools and technologies for multimodal data labeling

Multimodal data labeling is a supervised learning technique in which human labelers prepare the dataset by assigning labels that guide the model during training. This process was initially done manually, meaning human labelers would assign labels to the data by hand. However manual approaches are time-consuming and resource-intensive. For example, it would take many annotators several days to annotate enough data to train a mid-sized model of 5 billion parameters. 

The whole point of AI is automation and problem-solving, so researchers found a workaround for manual data labeling. Automated tools using machine learning algorithms and can guarantee faster and more accurate annotation under human supervision were introduced. 

An example of such a tool is the Labelbox's Label Blocks. This all-in-one labeling studio is designed to handle different data types, providing a unified annotation platform. Label Blocks supports labeling text, image, audio, geospatial, video, and sensor data. The platform supports multimodal labeling with project management, segmentation, quality control, and collaboration functionalities.    

How to label multimodal data

Training multimodal models starts with identifying the project's scope and modalities involved, then collecting data. Once we have consolidated diverse datasets from all modalities, we move straight to labeling. The labeling process is quite extensive for multimodal models as labeling techniques differ for each modality.

For text, we label data with tags, while for images, we annotate with bounding boxes and segmentation masks. On the other hand, sensor data is labeled through temporal alignment and labeling specific event instances. Conversely, audio data might first be transcribed and then labeled by classification.

Multimodal labeling requires an integrated platform like Label Blocks to handle different modalities. This tool offers editor interfaces that accept different attachment formats. The Label Blocks editor has global settings and enhancements, such as attachments, data row information panels, and instructions. 

The labeling process is quite simplified while using third-party tools like Label Blocks. After importing the multimodal datasets, we simply tweak the data row information panel and supplementary features to align with the labeling goals. The editor offers the superior functionality of attaching a labeling instruction document for various modalities. Alongside this instruction document, the labeler can attach additional context and information such as metadata, curated tags, and media attributes to expedite the labeling process. 

Simply put, labeling multimodal data entails understanding the data format and its elements. Platforms like Label Blocks provide highly customizable editors, making multimodal data labeling less hassle.  

Real world applications of multimodal data labeling

Multimodal models are the new normal in AI development, and so is the significance of multimodal data labeling. Developing these models requires us to have a large-scale annotated multimodal dataset. Various application areas need multimodal data labeling. Besides OpenAI’s GPT-4o model with natural language processing, video generation, and voice assistance capabilities, other industries also need multimodal data labeling.

Autonomous automobiles

In the manufacturing of autonomous self-driving automobiles, multimodal data labeling is critical. Since these systems leverage machine learning techniques, multimodal data from LIDAR, GPS, camera, and radar are consolidated to train the model. These data from diverse modalities must be accurately labeled to guarantee a navigation system that can handle object detection, decision-making in complex environments, and path planning.    

Augmented reality and virtual reality (VR/AR)

Multimodal data labeling is also applied in developing augmented reality (AR) and virtual reality (VR) systems. Annotating and labeling visual data, haptic feedback, and visual data makes it easier to develop models that capture various modalities. Such multimodal models result in AR/VR systems that give users immersive and interactive experiences. 

Challenges of multimodal data labeling 

Although multimodal data labeling leads to the advancement of AI, its implementation in model development is not without challenges. Some examples encountered during multimodal data annotation include:

Data synchronization and alignment

Multimodal data labeling is sometimes difficult as aligning all the modalities during labeling to achieve a visible relationship during model training is daunting. This challenge stems from the consolidated modalities operating at different resolutions and time scales. 

To address this challenge, it is crucial to align datasets from various sources in a uniform format that the models can take in during training.

Scalability

Given the enormous volume of multimodal data consolidated during labeling, scalability emerges as a significant challenge. Efficient multimodal data labeling and processing require optimized workflows and powerful computational resources. Most organizations may not have the technical and resource capacity to run such systems and annotate this data in-house. 

A solution to the scalability issue is using automated and semi-automated labeling platforms like Label Blocks. Such tools bridge the resource gap and manual effort required in labeling multimodal data for model development.  

A recap of multimodal data labeling 

Multimodal data labeling is propelling the development of next-generation multimodal models. It promises intelligent and adaptable systems that can make predictions based on data from language, vision, and sensory modalities. This process advances AI development, and as the field evolves, it will only expand. With extensive research around emerging trends like deep learning for automatic feature extraction during labeling, the process will get even better. As a result, we are set to witness groundbreaking AI solutions built from this technique.

Labelbox supports multimodal data labeling by offering a unified platform for images, videos, text, etc. It provides customizable workflows, diverse annotation tools, collaboration features, and seamless integration with AI pipelines, ensuring efficient and accurate annotation across various data types. Experience the benefits firsthand by trying Labelbox for free.