MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization
Overview
As a co-first author, I proposed MG-RWKV, a multi-granularity framework for Temporal Forgery Localization (TFL) that leverages the data-dependent state evolution of RWKV to achieve efficient full-sequence processing with linear O(T) complexity. It introduces 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 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 to reduce false positives in authentic regions. Extensive experiments on Lav-DF, TVIL, and Psynd demonstrate state-of-the-art performance with low computational cost.