Rethinking Video Token Compression with a Global Codebook: Learning Once, Compressing Everywhere

ONCE is a plug-in video-token compression framework that moves expensive compression work from per-video inference to an offline stage. It learns a frequency-aware global codebook in visual feature space once, then compresses incoming tokens through lightweight codebook lookup and aggregation. According to the abstract, experiments span multiple video-understanding benchmarks and diverse compression baselines, with ONCE retaining competitive task performance while recording the lowest inference latency among the compared methods. The supplied abstract does not report model names, compression ratios, accuracy values, latency measurements, codebook size, or implementation availability.