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Habeeb — Computer Vision Engineer

The document lists several GitHub-linked AI/ML projects and pipelines, detailing post-training alignment, jailbreaker defense, multimodal fine-tuning on small GPUs, a voice-mimicking post generator, and a document-to-audio workflow.

The Roadmap to Becoming a Computer Vision Engineer in 2026 - KDnuggets

A four-stage guide shows how to become a computer vision engineer, from spatial math and traditional CNNs to vision transformers, multimodal models, and edge deployment considerations. It emphasizes building end-to-end pipelines and understanding model limitations, with concrete exercises and example code.

GitHub - usamahz/computer-vision-engineering: A reading path for computer vision engineering, from photon to production. Primary sources in dependency order, evidence-graded claims, and a runnable benchmark per section. · GitHub

A guided, source-backed reading path for computer vision engineering, tracing a vision system from photon to production. It emphasizes primary-source evidence, benchmarks, and a structured order to study optics, sensors, image processing, color, noise, and classical vision algorithms.

Computer vision within everyone's reach | TI INSIDE Online

Translated title. Original: Visão computacional ao alcance de todos | TI INSIDE Online

Beonic presents Beonic Vision, an edge-computing, hardware-agnostic video analysis platform that converts existing CCTV networks into real-time, AI-driven sensors for crowd behavior, queue times, and space usage, enabling cost-effective, scalable deployment across varied venues with modular add-ons.

Computer Vision Syllabus 2026: Subjects, Topics & Learning Roadmap - Scaler

A 2026 computer vision syllabus outlines foundational topics, modern methods, and a 24-week roadmap to learn, build, and deploy CV systems with emphasis on data quality, governance, and practical portfolios.

From Pixels to Physical AI: Building the Visual Cortex for Intelligent Robots - OpenCV Live! 232 - OpenCV

A tutorial-style live session showing how OpenCV integrates with robotics to form a visual cortex, using depth cameras, Jetson, ROS 2, and vision-language models to enable spatial reasoning, navigation, and human-robot interaction, with open-source code and a course giveaway.

WeVisOne-VL: Unified Visual Perception from Objects to Pixels

WeVisOne-VL is a unified vision-language model that combines object- and pixel-level perception using structured next-token prediction, enabling tasks such as detection, localization, counting, pointing, OCR, segmentation, and matting at varying granularities.

Perceora — computer vision that runs on your own computer

Export and run the model on your hardware, score on new images, review mistakes, and train if desired. Name and label classes, verify and split data, add or prepare images, and set inspection parameters. Open Studio enables block-by-block customization; training may not be required for some tasks. Results depend on images, lighting, parts, and settings, so test locally before relying on it.

Seeing ahead through a state-of-the-art review of visual perception and predictive intelligence for collaborative robots | Artificial Intelligence Review | Springer Nature Link

The document reviews how visual perception and AI enable cobots to operate alongside humans in dynamic settings, highlighting current limits in clutter, occlusion, and real-time fusion, and proposing a three-direction research agenda to achieve plug-and-play, language-conditioned, data-efficient cobots.

[2610.26872] Commanding at the Speed of Sound: Audio-Centric Referring Expression Segmentation for Aerial Scenes

The paper shifts UAV referring expression segmentation from text-centric to audio-centric by using raw audio to guide pixel-level localization, introducing the Aerial-AudioRES benchmark and the ARES-Net model, which aligns acoustic signals with visual features and achieves strong accuracy with reduced latency.