CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models

arXiv:2601.04778 · cs.CV, cs.AI, cs.CL, cs.MM · Submitted 2026-01-08 · Read on arXiv

cs.CV, cs.AI, cs.CL, cs.MM

Submitted: 2026-01-08

Updated: 2026-08-27

Comments: EMNLP 2026

Project page: https://aimagelab.github.io/CounterVid

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Hallucination — 59× in this paper · explained in 3 episodes
• Benchmark — 34× in this paper · explained in 2 episodes
• Alignment — 14× in this paper · explained in 1 episode
• Grounding — 12× in this paper · explained in 3 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Temporal Reasoning — 6× in this paper · explained in 2 episodes
• Visual Grounding — 6× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Direct Preference Optimization — 3× in this paper
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Hallucination Mitigation — 2× in this paper · explained in 2 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• PE Video Dataset — 1× in this paper
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• Hallucination of Multimodal Large Language Models: A Survey
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Perception Encoder: The best visual embeddings are not at the output of the network
• PaMi-VDPO: Mitigating Video Hallucinations by Prompt-Aware Multi-Instance Video Preference Learning
• Large Language Models: A Survey
• Wan: Open and Advanced Large-Scale Video Generative Models
• VideoChat: Chat-Centric Video Understanding
• VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
• mDPO: Conditional Preference Optimization for Multimodal Large Language Models
• A Survey on Hallucination in Large Vision-Language Models
• VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models
• Qwen-Image Technical Report
• TempCompass: Do Video LLMs Really Understand Videos?
• EventHallusion: Diagnosing Event Hallucinations in Video LLMs
• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

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