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HyperClaim: Fine-Grained Cross-Modal Hypergraph Rea... | AI Research

Key Takeaways

  • HyperClaim is a framework designed to detect misinformation in short-form videos by analyzing localized, fine-grained interactions between text and visual fr...
  • Video misinformation detection is often approached through global multimodal fusion or free-form multimodal reasoning.
  • Both paradigms can under-represent localized authenticity cues that arise from coupled interactions among query phrases, contextual text, and short temporal spans of frames.
  • Because such interactions are inherently higher-order, pairwise graph formulations are insufficient to capture multi-way cross-modal dependencies, whereas hypergraphs offer a suitable representation for these relations.
  • We propose HyperClaim, a discriminative temporal hypergraph framework for sample-level authenticity classification.
Paper AbstractExpand

Video misinformation detection is often approached through global multimodal fusion or free-form multimodal reasoning. Both paradigms can under-represent localized authenticity cues that arise from coupled interactions among query phrases, contextual text, and short temporal spans of frames. Because such interactions are inherently higher-order, pairwise graph formulations are insufficient to capture multi-way cross-modal dependencies, whereas hypergraphs offer a suitable representation for these relations. We propose HyperClaim, a discriminative temporal hypergraph framework for sample-level authenticity classification. Using the title or benchmark-provided paired text as a claim-like query, HyperClaim constructs a sparse heterogeneous hypergraph over query tokens, evidence tokens, and sampled frames; applies confidence-aware filtering and source budgeting to form compact text-frame and short-range temporal evidence units; performs adaptive soft-incidence reasoning with residual text-video calibration; and aggregates textual, visual, and hyperedge states through a discrepancy-aware readout. Without relying on generated rationales or external tool calls, HyperClaim preserves fine-grained cross-modal and temporal structure that global fusion tends to flatten. Under the FactGuard temporal protocol, it achieves 83.7%, 82.0%, and 87.3% accuracy on FakeSV, FakeTT, and FakeVV, respectively, outperforming strong discriminative and reasoning-centric baselines. Learned incidence and attention weights further reveal token- and frame-level structure.

HyperClaim is a framework designed to detect misinformation in short-form videos by analyzing localized, fine-grained interactions between text and visual frames. Instead of condensing all video content into a single global summary—which can obscure specific inconsistencies—the authors propose using a hypergraph to represent higher-order relationships between query phrases, contextual text, and short temporal segments of video.

Addressing Localized Misinformation

Video misinformation often relies on subtle discrepancies, such as a mismatch between a title and a specific frame or a misleading temporal edit. Traditional methods that use global multimodal fusion often flatten these details, losing the evidence needed for accurate detection. HyperClaim treats the video title or provided text as a "claim-like query" and organizes the remaining multimodal evidence around it, preserving the structure of the original content without requiring external tools or generated rationales.

How HyperClaim Works

The framework operates through four primary stages:

  • Encoding: It uses a multimodal encoder to maintain token-level representations for text and chronological frame-level embeddings for video.

  • Hypergraph Formation (H-Forge): The system constructs a sparse heterogeneous hypergraph. It creates specific hyperedges for claim tokens, evidence tokens, and temporal windows of frames. It uses a confidence-aware filtering process to select the most relevant textual anchors and their corresponding visual matches, ensuring the graph remains focused on meaningful evidence.

  • Reasoning (Aether): This stage performs bidirectional calibration between text and video, followed by adaptive node-to-hyperedge reasoning. It uses soft-incidence weights to propagate information, allowing the model to learn which connections are most relevant to the authenticity of the video.

  • Readout (Cred): The final stage aggregates the textual, visual, and hyperedge states. It calculates both the agreement and the discrepancy between the claim and the video to predict whether the content is real or fake.

Performance and Evidence Tracing

In evaluations under the FactGuard temporal protocol, HyperClaim achieved 83.7% accuracy on FakeSV, 82.0% on FakeTT, and 87.3% on FakeVV. These results outperform existing discriminative and reasoning-centric baselines. Beyond classification, the model provides structural evidence traces; by examining learned incidence and attention weights, users can identify the specific tokens and frames that contributed most to the model's decision, offering transparency into how the system arrived at its conclusion.

Implementation Considerations

HyperClaim is designed for a "closed-input" setting, meaning it relies on the information already present within the video and its associated text. It does not perform open-ended web searches or external fact-checking. The authors utilize a two-stage optimization strategy: first training the reasoning and prediction modules on precomputed features, and then using LoRA adapters to fine-tune the multimodal encoder. This approach allows the model to adapt to specific evidence structures while maintaining a stable decision-making process.

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