Bhomik Sharma

SAM-3: What’s New, How It Works, and Why It Matters

Yet another SOTA model from META, meet SAM-3. Learn about what's new and how to implement your own tracking pipeline using SAM-3.

Generative AI, Image Segmentation

The Ultimate Guide to Vector DB and RAG Pipeline

Processing long documents with VLMs or LLMs poses a fundamental challenge: input size exceeds context limits. Even with GPUs, as large as 12 GB can barely process 3-4 pages at

Generative AI, VLMs

TRM: Tiny AI Models beating Giants on Complex Puzzles

Models with billions, or trillions, of parameters are becoming the norm. These models can write essays, generate code, as well as create art. But they can still get stuck on

Neural Attention, Transformer Neural Networks

The Ultimate Guide To VLM Evaluation Metrics, Datasets, And Benchmarks

Get a comprehensive overview of VLM Evaluation Metrics, Benchmarks and various datasets for tasks like VQA, OCR and Image Captioning.

Computer Vision, VLMs

AI for Video Understanding: From Content Moderation to Summarization

The rapid growth of video content has created a need for advanced systems to process and understand this complex data. Video understanding is a critical field in AI, where the

Computer Vision, Generative AI, Video Analysis, Vision Language Models

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 summarize

Agentic AI, AI Art Generation, Computer Vision, Generative AI, Generative Models, Hugging Face Transformers, Multimodal Models, Vision Language Models

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 CARLA Leaderboard 2.0 and

Advanced Driver Assistance Systems, Autonomous Vehicle, Computer Vision, Robotics, VLMs

Building an Agentic Browser with LangGraph: A Visual Automation and Summarization Pipeline

Developing intelligent agents, using LLMs like GPT-4o, Gemini, etc., that can perform tasks requiring multiple steps, adapt to changing information, and make decisions is a core challenge in AI development.

Agentic AI, Computer Vision, Generative AI, Generative Models, LLMs, VLMs

SigLIP 2: DeepMind’s Multilingual Vision-Language Model

SigLIP-2 represents a significant step forward in the development of multilingual vision-language encoders, bringing enhanced semantic understanding, localization, and dense feature extraction capabilities. Built on the foundations of SigLIP, this

Computer Vision, Generative AI, LLMs, NLP, VLMs

Optimizing VJEPA-2: Tackling Latency & Context in Real-Time Video Classification Scripts

The domain of video understanding is rapidly evolving, with models capable of interpreting complex actions and interactions within video streams. Meta AI’s VJEPA-2 (Video Joint Embedding Predictive Architecture) stands out

Generative AI, video classification, Vision Language Models

V-JEPA 2: Meta’s Breakthrough in AI for the Physical World

The ultimate goal for many in artificial intelligence is to build agents that can perceive, reason, and act in our complex physical world. Meta AI has made a significant stride

Computer Vision, Generative AI, Generative Models, Hugging Face Transformers, Multimodal Models, Robotics, Vision Language Models

The Definitive Guide to LLaVA: Inferencing a Powerful Visual Assistant

To develop AI systems that are genuinely capable in real-world settings, we need models that can process and integrate both visual and textual information with high precision. This is the

Multimodal Models, Vision Language Models, VLMs