open-vocabulary detection

**Open-Vocabulary Detection (OVD)** is an **object detection paradigm where models can locate and classify arbitrary objects** — described by free-form text, rather than being limited to a fixed list of base categories (like the 80 classes in COCO). **What Is Open-Vocabulary Detection?** - **Definition**: Detecting objects described by any text prompt. - **Core Idea**: Replace the final classification layer (logits) with a text-image alignment score (dot product). - **Training**: Uses base classes with bounding boxes + image-text pairs (captions) for vocabulary expansion. - **Inference**: User provides a list of potentially novel class names -> Model finds them. **Why OVD Matters** - **Flexibility**: Detect "pokemon", "r2d2", or "covid mask" without retraining. - **Language Integration**: Bridges the gap between NLP and Computer Vision. - **Search**: Enables powerful semantic search loopups in video/image databases. **Key Models** - **ViLD**: Vision and Language knowledge Distillation. - **GLIP**: Grounded Language-Image Pre-training. - **OWL-ViT**: Open-World Localization Vision Transformer. - **Grounding DINO**: State-of-the-art open-set detection. **Open-Vocabulary Detection** is **bringing search-like flexibility to vision** — allowing us to find anything we can name, essentially "Googling" the physical world.