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 goal is to enable ...
Latest From the Blog
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 ...
Object Detection and Spatial Understanding with VLMs ft. Qwen2.5-VL
Object Detection is predominantly a vision task where we train a vision model, like YOLO, to predict the location of the object along with its class. But still it depends on the pre-trained classes, ...
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 ...
Inside Sinusoidal Position Embeddings: A Sense of Order
In the groundbreaking 2017 paper "Attention Is All You Need", Vaswani et al. introduced Sinusoidal Position Embeddings to help Transformers encode positional information, without recurrence or ...
Inside RoPE: Rotary Magic into Position Embeddings
Self-attention, the beating heart of Transformer architectures, treats its input as an unordered set. That mathematical elegance is also a curse: without extra signals, the model has no idea which ...
