Absence is Presence: Understanding Visual Scene Negative Events Under Safety Cognitive Constraint

arXiv:2609.19812 · cs.CV · Submitted 2026-09-17 · Read on arXiv

cs.CV

Submitted: 2026-09-17

Updated: 2026-09-17

Code: https://github.com/hiyouga/LlamaFactory

Terminology

• Alignment — 13× in this paper · explained in 1 episode
• Amodal Completion — 11× in this paper · explained in 1 episode
• GRPO — 10× in this paper · explained in 6 episodes
• Representation Learning — 10× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Amodal Completion Branch — 9× in this paper
• Ablation study — 7× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Multi-Condition Representation Learning — 6× in this paper
• Adaptation — 5× in this paper · explained in 1 episode
• Grounding — 4× in this paper · explained in 3 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Core — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• LoRA Adapters — 3× in this paper · explained in 3 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Direct Preference Optimization — 2× in this paper
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Group Relative Policy Optimization — 2× in this paper
• Latent Representation — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• AI Integration — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Odds Ratio Preference Optimization — 1× in this paper
• Pragmatics — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Vanilla Supervised Fine-Tuning — 1× in this paper
• Visual Grounding — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection
• Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs
• Conditional Representation Learning for Customized Tasks
• Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends
• Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations
• Know "No" Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIP
• TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP
• NegVQA: Can Vision Language Models Understand Negation?
• Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
• Group Relative Policy Optimization for Image Captioning
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• GME: Improving Universal Multimodal Retrieval by Multimodal LLMs

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