MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization

• URL: http://arxiv.org/abs/2607.00902v1
• Date: Wed, 01 Jul 2026 13:04:35 GMT
• Title: MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization
• Abstract summary: MG-RWKV is a multi-granularity framework for full-sequence processing with O(T) complexity.<n>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
• Score: 43.92649543343891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• EVAS: Efficient Multimodal Temporal Forgery Localization via Audio-Visual Synergy and Steered Boundary Calibration [14.088182834458843]
  We propose EVAS, an end-to-end framework for temporal forgery localization.<n>We introduce a Boundary-Aware Refinement strategy to achieve steered boundary calibration.<n> EVAS achieves state-of-the-art average localization accuracy and average recall across three benchmark datasets.
  arXiv  Detail & Related papers  (2026-07-05T19:33:21Z)
• MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection [108.36437360254605]
  This paper proposes MambaADv2, a framework tailored for multi-class unsupervised anomaly detection.<n>MambaADv2 comprises a pre-trained encoder and a Mamba-inspired decoder, equipped with Duality-enhanced State Space (DSS) modules across multiple scales.<n>The structure of the Hybrid State Space (HSS) block is tailored by following the SSD-based Mamba lineage and incorporating Mamba3-style position-aware state-space modeling.
  arXiv  Detail & Related papers  (2026-06-22T10:14:06Z)
• ReHARK: Refined Hybrid Adaptive RBF Kernels for Robust One-Shot Vision-Language Adaptation [3.8707695363745214]
  ReHARK is a training-free framework that reinterprets few-shot adaptation through global proximal regularization.<n>A new state-of-the-art for one-shot adaptation is established by ReHARK, which achieves an average accuracy of 65.83%.
  arXiv  Detail & Related papers  (2026-03-12T04:59:09Z)
• StepVAR: Structure-Texture Guided Pruning for Visual Autoregressive Models [98.72926158261937]
  We propose a training-free token pruning framework for Visual AutoRegressive models.<n>We employ a lightweight high-pass filter to capture local texture details, while leveraging Principal Component Analysis (PCA) to preserve global structural information.<n>To maintain valid next-scale prediction under sparse tokens, we introduce a nearest neighbor feature propagation strategy.
  arXiv  Detail & Related papers  (2026-03-02T11:35:05Z)
• OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL [63.388513841293616]
  Existing forgery detection methods fail to handle the interleaved text, images, and videos prevalent in real-world misinformation.<n>To bridge this gap, this paper targets to develop a unified framework for omnibus vision-language forgery detection and grounding.<n>We propose textbf OmniVL-Guard, a balanced reinforcement learning framework for omnibus vision-language forgery detection and grounding.
  arXiv  Detail & Related papers  (2026-02-11T09:41:36Z)
• SDiT: Semantic Region-Adaptive for Diffusion Transformers [4.7254170106792035]
  Diffusion Transformers (DiTs) achieve state-of-the-art performance in text-to-image synthesis but remain computationally expensive due to the iterative nature of denoising and the quadratic cost of global attention.<n>We propose SDiT, a Semantic Region-Adaptive Diffusion Transformer that allocates computation according to regional complexity.
  arXiv  Detail & Related papers  (2026-01-18T06:43:36Z)
• Fourier-RWKV: A Multi-State Perception Network for Efficient Image Dehazing [26.57698394898644]
  We propose a novel dehazing framework based on a Multi-State Perception paradigm.<n>Fourier-RWKV delivers state-of-the-art performance across diverse haze scenarios.
  arXiv  Detail & Related papers  (2025-12-09T01:35:56Z)
• Scaling Implicit Fields via Hypernetwork-Driven Multiscale Coordinate Transformations [0.0]
  Implicit Neural Representations (INRs) have emerged as a powerful paradigm for representing signals such as images, 3D shapes, signed distance fields, and radiance fields.<n>This work introduces Hyper-Coordinate Implicit Neural Representations (HC-INR), a new class of INRs that break the representational bottleneck by learning signal-adaptive coordinate transformations using a hypernetwork.
  arXiv  Detail & Related papers  (2025-11-23T10:27:04Z)
• Bidirectional Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression [97.66080040613726]
  We propose a Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space.<n>Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations.<n>We show Bi-FMT surpasses D-DPCC and AdaDPCC in both compression efficiency and runtime.
  arXiv  Detail & Related papers  (2025-09-18T03:51:06Z)
• TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models [14.411646409316624]
  We introduce textbfHierarchical textbfText-textbfFree textbfAlignment (textbfTS-HTFA), a novel method for time-series forecasting.<n>We replace paired text data with adaptive virtual text based on QR decomposition word embeddings and learnable prompt.<n>Experiments on multiple time-series benchmarks demonstrate that HTFA achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-09-23T12:57:24Z)
• Temporal Context Aggregation Network for Temporal Action Proposal Refinement [93.03730692520999]
  Temporal action proposal generation is a challenging yet important task in the video understanding field. Current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval. We propose TCANet to generate high-quality action proposals through "local and global" temporal context aggregation.
  arXiv  Detail & Related papers  (2021-03-24T12:34:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.