Object Embeddings

Object embeddings represent objects as numerical vectors, aiming to capture their visual and semantic characteristics for tasks like object recognition, retrieval, and tracking. Current research focuses on improving embedding robustness to variations in viewpoint, pose, object state, and even across different sensor modalities (e.g., LiDAR, tactile sensors), often employing techniques like contrastive learning, attention mechanisms, and graph neural networks within various architectures (e.g., dual encoders, transformers). These advancements are crucial for improving the performance of computer vision and robotics systems, enabling more accurate and reliable object understanding in complex and dynamic environments.