momenta-multimodal-moe-misinformation-detection
MOMENTA — Mixture-of-Experts over multimodal embeddings with neural temporal aggregation for misinformation detection. Combines modality-specific MoE modules, bidirectional co-attention, discrepancy-aware branch, and attention-based temporal aggregation with drift/momentum encoding. Use when: multimodal misinformation detection, MoE for multimodal learning, temporal aggregation, cross-modal disagreement detection, fact-checking systems. Trigger: misinformation detection, multimodal MoE, cross-modal disagreement, temporal drift, fake news detection, MOMENTA, 多模态虚假信息检测.
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- hiyenwong/ai_collection
- ソースの最終更新活動
- 2026年6月4日 13:32
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ソースの指示 · 読み取り専用プレビュー- name
- momenta-multimodal-moe-misinformation-detection
- description
- MOMENTA — Mixture-of-Experts over multimodal embeddings with neural temporal aggregation for misinformation detection. Combines modality-specific MoE modules, bidirectional co-attention, discrepancy-aware branch, and attention-based temporal aggregation with drift/momentum encoding. Use when: multimodal misinformation detection, MoE for multimodal learning, temporal aggregation, cross-modal disagreement detection, fact-checking systems. Trigger: misinformation detection, multimodal MoE, cross-modal disagreement, temporal drift, fake news detection, MOMENTA, 多模态虚假信息检测.
- version
- 1.0.0
- author
- Research Synthesis (arXiv:2604.16172)
- license
- MIT
- metadata
- {"hermes":{"tags":["misinformation","multimodal","mixture-of-experts","temporal-aggregation","fact-checking"],"source_paper":"MOMENTA: Mixture-of-Experts Over Multimodal Embeddings with Neural Temporal Aggregation for Misinformation Detection (arXiv:2604.16172)"}}
# MOMENTA: Multimodal MoE Misinformation Detection
## Overview
Unified multimodal misinformation detection framework combining:
- Modality-specific MoE modules for specialized processing
- Bidirectional co-attention for text-visual alignment
- Discrepancy-aware branch for cross-modal disagreement
- Attention-based temporal aggregation with drift/momentum encoding
- Domain-adversarial learning with prototype memory bank
## Architecture
```
┌─────────────────────────────────────────────────────────┐
│ Input Modalities │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ Text │ │ Visual │ │ Temporal │ │
│ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
│ ↓ ↓ ↓ │
│ ┌──────────┐ ┌──────────┐ │
│ │ Text MoE │ │Vis MoE │ ← Modality-specific experts │
│ └────┬─────┘ └────┬─────┘ │
│ └──────┬──────┘ │
│ ↓ │
│ ┌─────────────────────┐ │
│ │ Bidirectional │ ← Cross-modal alignment │
│ │ Co-Attention │ │
│ └──────────┬──────────┘ │
│ ↓ │
│ ┌─────────────────────┐ ← Detect contradictions │
│ │ Discrepancy-Aware │ between modalities │
│ │ Branch │ │
│ └──────────┬──────────┘ │
│ ↓ │
│ ┌─────────────────────┐ ← Drift/momentum encoding │
│ │ Temporal Aggregation│ for temporal sequences │
│ │ (Drift/Momentum) │ │
│ └──────────┬──────────┘ │
│ ↓ │
│ ┌─────────────────────┐ ← Domain generalization │
│ │ Domain-Adversarial │ + prototype memory │
│ │ + Prototype Memory │ │
│ └──────────┬──────────┘ │
│ ↓ │
│ Misclassification (Real/Fake) │
└─────────────────────────────────────────────────────────┘
```
## Key Components
### 1. Modality-Specific MoE
- Separate expert pools for text and visual modalities
- Dynamic routing selects relevant experts per input
- Enables specialized feature extraction
### 2. Bidirectional Co-Attention
- Text attends to visual features AND visual attends to text
- Creates aligned cross-modal representations
- Captures inter-modal dependencies
### 3. Discrepancy-Aware Branch
- Specifically detects disagreements between modalities
- Key signal: text says one thing, image shows another
- Primary misinformation indicator
### 4. Temporal Aggregation
- Drift encoding: captures distribution shift over time
- Momentum encoding: maintains temporal consistency
- Attention-weighted aggregation of temporal features
## Evaluated Datasets
- Fakeddit, MMCoVaR, Weibo, XFacta
## Applications
- Social media content moderation
- Automated fact-checking systems
- News verification platforms
- Multimodal content analysis
## Activation Keywords
- misinformation detection, multimodal MoE, fact-checking
- cross-modal disagreement, temporal aggregation
- fake news detection, MOMENTA
- 虚假信息检测, 多模态专家混合
## References
- Yeganeh Abdollahinejad, Ahmad Mousavi, et al. "MOMENTA: Mixture-of-Experts Over Multimodal
Embeddings with Neural Temporal Aggregation for Misinformation Detection." arXiv:2604.16172
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