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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 –
Zero-shot anomaly detection (ZSAD) is a vital problem in computer vision, particularly in real-world scenarios where labeled anomalies are scarce or unavailable. Traditional vision-language models (VLMs) like CLIP fall short
This article discusses how to train a CLIP like model from scratch. It presents gradio app for Fashion E-commerce Image Retrieval using Text search in PyTorch.
DeepSeek OCR Paper Explanation and Test using Transformers and vLLM Pipeline. Understanding Context Optical Compression and model architecture in depth.
Discover VideoRAG, a framework that fuses graph-based reasoning and multi-modal retrieval to enhance LLMs' ability to understand multi-hour videos efficiently.
Get a comprehensive overview of VLM Evaluation Metrics, Benchmarks and various datasets for tasks like VQA, OCR and Image Captioning.
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
Learn how Video-RAG boosts training-free and low-compute long-video understanding by pairing OCR, ASR, and open-vocabulary detection with any long-video LVLMs.
What if a radiologist facing a complex scan in the middle of the night could ask an AI assistant for a second opinion, right from their local workstation? This isn't
In the evolving landscape of open-source language models, SmolLM3 emerges as a breakthrough: a 3 billion-parameter, decoder-only transformer that rivals larger 4 billion-parameter peers on many benchmarks, while natively supporting
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
