Class Detection

Class detection, encompassing both known and unknown classes, aims to accurately identify and categorize objects within images or other data modalities. Current research focuses on improving the robustness of detectors to novel classes, employing techniques like part-based attention mechanisms, incremental learning strategies with balanced loss functions, and novelty detection methods based on density estimation in latent space or Siamese networks. These advancements are crucial for real-world applications such as autonomous driving, biodiversity monitoring, and medical image analysis, where encountering unseen classes is common and accurate classification is paramount.