LiteVLM: A Low-Latency Vision-Language Model Inference Pipeline for Resource-Constrained Environments

The introduction of LiteVLM marks a significant advancement in the field of vision-language models, particularly for resource-constrained environments like robotics and autonomous driving. This innovative pipeline optimizes performance by reducing computational demands, making it easier to deploy on embedded devices. By filtering irrelevant camera views and streamlining input sequences, LiteVLM not only enhances efficiency but also accelerates token generation. This development is crucial as it opens up new possibilities for integrating advanced AI capabilities into everyday technology, potentially transforming how machines understand and interact with the world.