MG-RWKV combines bidirectional RWKV, multi-granularity mixture of experts, and cross-granularity consistency to achieve state-of-the-art temporal forgery localization with linear complexity.
In: Conference on Language Modeling (COLM) (2024)
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FreeMEF is the first flexible-frame transformer for multi-exposure fusion using a recurrent state space module and global feature guided block to handle variable numbers of input exposures.
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MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization
MG-RWKV combines bidirectional RWKV, multi-granularity mixture of experts, and cross-granularity consistency to achieve state-of-the-art temporal forgery localization with linear complexity.
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There and Back Again: A Flexible-Frame Transformer for Multi-Exposure Fusion
FreeMEF is the first flexible-frame transformer for multi-exposure fusion using a recurrent state space module and global feature guided block to handle variable numbers of input exposures.