Bounding Boxes vs Segmentation Masks: Where Annotation QA Matters More
Exploring the impact of annotation quality on bounding boxes and segmentation masks.
10 Popular Public LiDAR Datasets for Autonomous Vehicles
Best 10 open-source LiDAR datasets for self-driving vehicles: essentials, features, and uses.
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.
SAM 3: Key Challenges in Video Annotation Tracking
Applying SAM 3 to video annotation and object tracking looks powerful on paper, but
Top 10 OCR Applications Across Industries
Top 10+ Real-World Applications of OCR, NLP, and Multi-modal AI Across Industries. With examples and a sample dataset.
Data Annotation QA Agents in 2026: Teaching Computer Vision Datasets to Self-Correct
Annotation QA Agents: Architecture, Self-Correction Mechanisms, and Real-World Use Cases.
![Multimodal AI in Robotics [+ Examples]](/content/images/size/w2000/2026/01/Robotics--12-.png)
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.
OCR: Essentials, Workings, Types, Challenges
A deep dive into Optical Character Recognition (OCR): its essentials, workings, types, approaches, and challenges.
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.