MG-RWKV: 時間的偽造位置特定のための多粒度文脈認識RWKV
MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization
RWKVの状態進化を利用し、O(T)計算量で全シーケンス処理を実現する多粒度フレームワークを提案。双方向RWKV、多粒度MoE、粒度間一貫性により、偽造セグメントの正確な特定を実現した。
著者: Jingchen Ni, Cangjin Yu, Dan Jiang, Quan Zhang, Keyu Lv, Shannan Yan, Linyue Pan, Ke Zhang, Chun Yuan
分類: cs.CV
原文アブストラクト
Driven by Artificial Intelligence-Generated Content (AIGC), the authenticity of audio-visual content is facing severe challenges. Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments within untrimmed sequences. However, existing methods are limited by CNNs' local receptive fields or Transformers' quadratic complexity, while emerging linear models often struggle to balance global authentic context compression with local abrupt forgery perception. To address this, we propose MG-RWKV, a multi-granularity framework that leverages the data-dependent state evolution of RWKV to achieve efficient full-sequence processing with O(T) complexity. Our framework features three core innovations: (1) a Bidirectional RWKV architecture that captures bidirectional temporal contexts without quadratic overhead; (2) a Multi-Granularity Mixture of Experts (MG-MoE) that performs dynamic routing over explicit temporal receptive fields, adaptively selecting granularities based on forgery duration to significantly enhance decision interpretability; and (3) Cross-Granularity Consistency (CGC), which aligns adjacent feature pyramid levels through hierarchical scale-wise pairing and spatial boundary-aware weighting, effectively reducing false positives in authentic regions. Extensive experiments on Lav-DF, TVIL, and Psynd datasets demonstrate that MG-RWKV achieves state-of-the-art performance with low computational cost.