OmniSelect: Dynamic Modality-Aware Token Compression for Efficient Omni-modal Large Language Models
OmniSelect is a training-free method for compressing long audio-video sequences in OmniLLMs. It dynamically selects compression strategies—Audio-Centric, Video-Centric, or Uniform—based on cross-modal relevance estimated by AudioCLIP. This adaptive pruning preserves model accuracy while significantly speeding up inference and reducing memory usage.