OpenVAM: Open-World Visual Attention Modeling with VLMs

arXiv:2609.31364 · cs.CV · Submitted 2026-09-25 · Read on arXiv

cs.CV

Submitted: 2026-09-25

Updated: 2026-09-25

Project page: https://k-hooshanfar.github.io/openvam/Abstract

Terminology

• Grounding — 31× in this paper · explained in 3 episodes
• Consistency — 19× in this paper · explained in 3 episodes
• Benchmark — 18× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Transformer — 12× in this paper · explained in 2 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Generalization — 8× in this paper · explained in 13 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Adaptation — 5× in this paper · explained in 1 episode
• Hallucination — 5× in this paper · explained in 3 episodes
• Unified Framework — 5× in this paper · explained in 5 episodes
• Domain Shift — 4× in this paper · explained in 4 episodes
• Core — 3× in this paper · explained in 2 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Explainability — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Visual Grounding — 2× in this paper · explained in 2 episodes
• Active Inference — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Jensen–Shannon Divergence (JSD) — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research
• How is Gaze Influenced by Image Transformations? Dataset and Model
• Eye Gaze Tells You Where to Compute: Gaze-Driven Efficient VLMs
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• DTFSal: Audio-Visual Dynamic Token Fusion for Video Saliency Prediction
• Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
• DINOv3

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