Learnable Token

Learnable tokens are emerging as a powerful technique in various deep learning models, primarily aiming to improve efficiency and performance by selectively processing or representing information within the model. Current research focuses on integrating learnable tokens into transformer architectures for tasks like image segmentation, video grounding, and language modeling, often employing techniques like cross-attention mechanisms and specialized training strategies to optimize their effectiveness. This approach shows promise in addressing computational bottlenecks in large models, enhancing the accuracy of downstream tasks, and enabling novel applications such as efficient context compression in LLMs and improved handling of imbalanced data in text generation.