Instance Embeddings

Instance embeddings represent a crucial area of research focused on creating unique, descriptive vector representations for individual objects or instances within data, such as images or sentences. Current research emphasizes improving the quality and discriminative power of these embeddings through techniques like contrastive learning, sparse coding, and diverse global representation modeling, often integrated into architectures such as Multiple Instance Learning (MIL) frameworks or query-based segmentation models. These advancements are driving improvements in various applications, including whole slide image classification, novel object detection and segmentation, and video instance segmentation, by enabling more accurate and efficient object recognition and tracking.