Benchmarking MLLMs via Cognitive Expected Scene Graph for Safety-Critical Visual Negation Understanding

arXiv:2609.19767 · cs.CV · Submitted 2026-09-17 · Read on arXiv

cs.CV

Submitted: 2026-09-17

Updated: 2026-09-17

Project page: https://qwenlm.github.io

Terminology

• Benchmark — 11× in this paper · explained in 2 episodes
• Alignment — 10× in this paper · explained in 1 episode
• Benchmarking — 8× in this paper · explained in 3 episodes
• Cognitive Expected Scene Graph — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Ablation study — 4× in this paper · explained in 1 episode
• Consistency — 4× in this paper · explained in 3 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Affirmative Caption — 2× in this paper
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Supervised Fine-Tuning — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Arbiter — 1× in this paper · explained in 1 episode
• Decode — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Pairwise Accuracy — 1× in this paper
• Pearson Correlation Coefficient — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes

Sources

• Qwen3-VL Technical Report
• TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Benchmarking and Improving Detail Image Caption
• Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs
• GPT-4o System Card
• Know "No" Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIP
• Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives
• Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• When and why vision-language models behave like bags-of-words, and what to do about it?
• NegVQA: Can Vision Language Models Understand Negation?

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