open-world detection

**Open-World Detection** is a **vision task where models must detect known objects while identifying "unknown" objects as novel** — and incrementally learn these new classes when labeled data becomes available, creating a continuous learning loop. **What Is Open-World Detection?** - **Definition**: Detect Knowns + Detect Unknowns + Learn New Classes. - **Challenge**: Standard detectors force every detection into a known class (or background). - **The "Unknown" Label**: The model essentially says, "I see an object here, but I don't have a name for it yet." - **Incremental Learning**: Updating the model to name the unknowns without forgetting old classes. **Vs. Open-Vocabulary**: - **Open-Vocabulary**: Uses text embeddings to match *named* novel classes immediately. - **Open-World**: Detects *unnamed* novel objects as "Unknown" first. **Why It Matters** - **Robotics**: A robot must stop for an obstacle even if it doesn't know what it is. - **Autonomous Driving**: Safety criticality requires detecting anomalies/foreign objects. - **Discovery**: Helps mine datasets for missing categories. **Open-World Detection** is **critical for autonomous safety** — acknowledging that the AI's knowledge is incomplete and handling the unknown gracefully rather than confidently misclassifying it.