Gen2Balance: text-to-video synthesis fills long-tailed data for SOTA action recognition
dimadamen · x · 2026-09-10
Gen2Balance (Univ. of Bristol & Adobe Research, ECCV 2026) tackles long-tailed video action recognition by generating synthetic clips with text-to-video models. An MLLM analyzes real exemplars to write diverse, class-faithful prompts, and a two-stage training strategy mitigates synthetic domain shift. With a released dataset of 140K generated clips across 223 classes, it beats strong baselines on UCF-LT and K100-LT, with large gains on tail and few-shot actions.
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