Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models
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 representations through a lightweight MLP-style network. Specifically, we use a contrastive random walk appro
The rapid advancement and deployment of Video Large Multimodal Models are exposing their limitations, specifically hallucinations, necessitating immediate research into mitigation techniques to improve reliability.
Improving the accuracy and trustworthiness of video-based AI models is crucial for their broader adoption in critical applications, ranging from autonomous systems to content generation.
This research suggests a more efficient, less latent-inducing method for mitigating hallucinations in video models, potentially accelerating their real-world utility.
- · AI developers
- · Video analytics companies
- · Industries relying on video AI
- · Developers relying solely on heuristic intervention methods
More reliable video large multimodal models become available for various applications.
Increased trust in AI-generated video analysis leads to wider adoption across sectors like security and entertainment.
The reduced risk of AI hallucinations accelerates the integration of these models into decision-making systems, impacting policy and operational efficiency.
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Read at arXiv cs.AI