In 2018, Pete Warden from TensorFlow Lite said, “The future of machine learning is tiny.” Today, with AI moving towards powerful Vision Language Models (VLMs), the need for high computing power has ...
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AnomalyCLIP : Harnessing CLIP for Weakly-Supervised Video Anomaly Recognition
Video Anomaly Detection (VAD) is one of the most challenging problems in computer vision. It involves identifying rare, abnormal events in videos - such as burglary, fighting, or accidents - amidst ...
Video-RAG: Training-Free Retrieval for Long-Video LVLMs
Long videos are brutal for today’s Large Vision-Language Models (LVLMs). A 30-60 minute clip contains thousands of frames, multiple speakers, on-screen text, and objects that appear, disappear, and ...
LangGraph: Building Self-Correcting RAG Agent for Code Generation
Welcome back to our LangGraph series! In our previous post, we explored the fundamental concepts of LangGraph by building a Visual Web Browser Agent that could navigate, see, scroll, and ...
SimLingo: Vision-Language-Action Model for Autonomous Driving
SimLingo is a remarkable model that combines autonomous driving, language understanding, and instruction-aware control—all in one unified, camera-only framework. It not only delivered top rankings on ...
FineTuning Gemma 3n for Medical VQA on ROCOv2
The release of Gemma 3n, Google's latest family of open nano models, made LLM edge deployment more accessible. Its unique architecture is engineered to address the persistent challenges ...
