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

2024-04-30 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

Spatiotemporal-Semantic Contrastive Decoding (SSCD) is a novel decoding strategy designed to mitigate hallucinations in Video Large Language Models (VideoLLMs) like Video-LLaVA (8 frames) and LLaVA-NeXT-Video (16 frames), both with 7B parameters. Hallucinations, where models generate outputs inconsistent with explicit video content or factual evidence, undermine reliability. SSCD addresses this by constructing "negative" video features that deliberately disrupt spatiotemporal consistency and semantic alignment. These disrupted features are then used in a contrastive decoding process during inference to suppress hallucinated outputs, without requiring retraining the backbone model. Extensive experiments on benchmarks such as VideoHallucer, EventHallusion, and VideoHallu demonstrate SSCD's effectiveness in reducing hallucinations while preserving general video understanding and reasoning capabilities on ActivityNet-QA and MMVU.

Key takeaway

For AI Scientists and ML Engineers deploying VideoLLMs, SSCD offers a robust method to enhance model reliability by significantly reducing hallucinations. You should consider integrating this decoding strategy, which avoids costly model retraining, to improve consistency with video content. Calibrate hyperparameters like contrastive strength (alpha, e.g., 0.8 for Video-LLaVA, 0.4 for LLaVA-NeXT-Video) and plausibility (beta, e.g., 0.1) to balance hallucination mitigation with preserving general video understanding and reasoning, ensuring your models provide more accurate and trustworthy outputs in complex scenarios.

Key insights

SSCD mitigates VideoLLM hallucinations by using deliberately disrupted spatiotemporal and semantic negative features in contrastive decoding.

Principles

• Hallucination mitigation benefits from negative projections that induce hallucinations.
• Disrupting spatiotemporal and semantic consistency creates effective negative features.
• Contrastive decoding can suppress hallucinations without model retraining.

Method

A lightweight Spatiotemporal-Semantic Disruptor perturbs video features by weakening cross-temporal/spatial associations via random walks and minimizing conditional mutual information with the target text, then applies contrastive decoding.

In practice

• Integrate a lightweight disruptor for inference-time hallucination control.
• Calibrate contrastive strength (alpha) and plausibility (beta) for optimal results.

Topics

• Video Large Language Models
• Hallucination Mitigation
• Contrastive Decoding
• Spatiotemporal Consistency
• Semantic Alignment
• Model Reliability

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• MACD: Model-Aware Contrastive Decoding via Counterfactual Data — MACD uses model feedback to create targeted counterfactual data, improving contrastive decoding for Video-LLM hallucination.
• Mitigating Hallucination in Vision-Language Models through Barrier-Regulated Adaptive Closed-form Steering — BRACS adaptively corrects LVLM hallucination by monitoring visual grounding and applying closed-form steering only when needed.
• The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs? — Contrastive decoding's MLLM hallucination mitigation gains are spurious, driven by output bias and sampling degradation, not true reduction.
• Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift — Stage-wise preference optimization with targeted data construction effectively reduces VLM hallucination by focusing on difficult grounding boundaries.
• HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models — Analyzing hidden evidence from D-LLM denoising trajectories significantly improves hallucination detection and interpretability.
• Mitigating Hallucinations in Large Language Models Via Decoder Layer Skipping — DeLask mitigates LLM hallucinations by dynamically skipping deeper decoder layers identified via gradient driftance.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.