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

Researchers introduce ViSSRes, an inference-time intervention method that reduces hallucinations in Video Large Multimodal Models by enhancing video representations through a lightweight MLP network. The approach achieves a 40.69% reduction in hallucination rates on LLaVA-NeXT-Video while improving video understanding by 18.36%, with minimal computational overhead during inference.

Video Large Multimodal Models represent a significant frontier in AI research, combining visual and language understanding to interpret video content. However, these models frequently produce hallucinated outputs—generating plausible but factually incorrect information about video content. This limitation undermines their reliability for critical applications, from automated content moderation to accessibility features. ViSSRes addresses this core vulnerability through a novel approach that maintains the model's original architecture while adding lightweight residual learning.

The method's technical innovation lies in its dual-perspective optimization strategy. By employing contrastive random walks to measure spatiotemporal consistency and conditional mutual information to align representations with semantic understanding, ViSSRes captures both the structural properties of videos and the model's conceptual knowledge. This dual anchoring prevents the model from generating outputs disconnected from actual video content. The inference-time intervention design is particularly significant because it avoids expensive retraining cycles, making the solution immediately applicable to existing deployed models.

For the AI industry, ViSSRes demonstrates that hallucination mitigation doesn't require architectural overhauls or substantial computational increases. The 40.69% reduction in hallucination rates on EventHallusion benchmarks suggests meaningful practical improvements. The 18.36% performance gain on MMVU under chain-of-thought reasoning indicates enhanced reasoning capabilities alongside reduced false content generation. These results validate that lightweight intervention methods can address fundamental model limitations effectively.

Looking forward, this research likely influences how developers optimize multimodal models for production environments. The approach establishes a framework for post-hoc model enhancement without retraining, applicable beyond video understanding to other multimodal contexts. Success in this direction could accelerate enterprise adoption of video AI systems across media, surveillance, and education sectors.

• →ViSSRes reduces hallucination rates in video understanding models by 40.69% using lightweight residual learning during inference
• →The method maintains frozen model backbones while optimizing video representations for spatiotemporal and semantic consistency
• →Single forward-pass inference requires minimal additional computational cost compared to existing contrastive decoding approaches
• →Performance improvements of 18.36% on MMVU benchmarks demonstrate enhanced video understanding alongside hallucination mitigation
• →Inference-time intervention design enables immediate application to already-deployed video multimodal models without retraining