Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models

📰 ArXiv cs.AI

Learn to mitigate hallucinations in video large multimodal models using ViSSRes, a spatiotemporal-semantic residual method

advanced Published 8 Jun 2026

Action Steps

Implement ViSSRes to enhance video representations
Apply spatiotemporal-semantic residual to mitigate hallucinations
Evaluate the performance of ViSSRes using metrics such as accuracy and inference latency
Compare ViSSRes with existing inference-time intervention methods
Integrate ViSSRes into video large multimodal models to improve video understanding

Who Needs to Know This

AI engineers and researchers working on video understanding models can benefit from this method to improve model performance and reduce hallucinations

Key Insight

💡 ViSSRes enhances video representations using spatiotemporal-semantic residual to reduce hallucinations in video large multimodal models

Key Takeaways

Learn to mitigate hallucinations in video large multimodal models using ViSSRes, a spatiotemporal-semantic residual method

Full Article

Title: Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models

Abstract:
arXiv:2601.22574v2 Announce Type: replace-cross Abstract: Although Video Large Multimodal Models have achieved strong performance in video understanding, they still suffer from hallucination. Existing inference-time intervention methods usually modify videos under the contrastive decoding framework, but their heuristic designs bring limited improvements and increase inference latency. To address these issues, we propose ViSSRes, an inference-time intervention method that enhances video represent

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