Asad Iqbal

Asad is a technical writer at Unitlab, with experience writing documentation on how to train and use state-of-the-art computer vision models.

Multimodal AI Robotics & Physical AI

Multimodal AI in Robotics [+ Examples]

Multimodal AI in robotics is an AI approach where robots fuse multiple sensor inputs to perceive and act. By combining visual data, language, and other signals, robots make real-time, context-aware decisions.

13 min read

Video & Multiview Computer Vision

The Hidden Cost of Poor Video Annotation (And How to Fix It)

Poor video annotation creates noisy annotated data. Small labeling errors across video frames break temporal context and tracking consistency. Studies show that model accuracy can drop from 95% to nearly 74% when trained on low-quality annotations rather than high-quality annotated data.

8 min read

Featured illustration for “High-Performance Video Annotation for Computer Vision”

Annotate video SAM3

High-Performance Video Annotation for Computer Vision | Unitlab AI

Video annotation for computer vision is the process of labeling objects, actions, or regions in video frames to create ground-truth data for computer vision models. It involves drawing bounding boxes, polygons, segmentation masks, or keypoints on objects of interest in each frame.

7 min read

physical ai Multimodal AI

Physical AI: Perception Stacks, Failure Modes, and Dataset Needs

Physical AI refers to AI-powered systems that operate in the real, physical world. These systems integrate sensors like cameras and LiDAR, with machine learning so they can perceive their surroundings and take actions in real time.

12 min read

Multimodal AI multimodal AI models

Top 15 Multimodal Models in 2026 (Open Source & Proprietary)

Multimodal models are AI systems that process and integrate multiple data types in parallel. They combine text, images, and audio into one unified language model or network. This lets them handle tasks like image captioning and visual question answering by combining visual cues and textual data.

19 min read

Featured illustration for “Top 7 Video Annotation Tools & Platforms for 2026”

Data Annotation Video & Multiview

Top 15+ Multimodal Datasets

Multimodal data is data from multiple modalities, such as text, images, audio, video, and sensors, combined so AI can understand the same event or object with richer context than any single source alone.

18 min read

Multimodal AI multimodal AI models

Top 30+ Real-World Multimodal Applications Across Industries

Multimodal applications use multimodal AI systems to combine multiple data types within a single model. By integrating diverse data modalities through data fusion, multimodal AI provide understanding of complex, real-world scenarios than unimodal AI.

13 min read

Multimodal AI multimodal data

The Ultimate Guide to Multimodal AI [Technical Explanation & Use Cases]

Multimodal AI processes and combines multimodal data at the same time. Multimodal AI systems gain richer context by aligning visual data, textual data, and other input data and handle complex tasks like image captioning, visual search, and generate human-sounding outputs, than unimodal AI systems.

15 min read