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cs.CV2026
Learning to See through Illumination Extremes with Event Streaming in Multimodal Large Language Models
Baoheng Zhang, Jiahui Liu, Gui Zhao +7
Multimodal Large Language Models (MLLMs) perform strong vision-language reasoning under standard conditions but fail in extreme illumination, where RGB inputs lose irrevocable stru…
cs.CV2026
Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
Mingle Zhou, Jiahui Liu, Jin Wan +2
Unsupervised Continuous Anomaly Detection (UCAD) is gaining attention for effectively addressing the catastrophic forgetting and heavy computational burden issues in traditional Un…
cs.CV2026
DIVER: Dynamic Iterative Visual Evidence Reasoning for Multimodal Fake News Detection
Weilin Zhou, Zonghao Ying, Chunlei Meng +6
Multimodal fake news detection is crucial for mitigating adversarial misinformation. Existing methods, relying on static fusion or LLMs, face computational redundancy and hallucina…