Amplify What You Gaze At: Target Saliency Boosting in Text-to-Image Generation

arXiv:2609.30733 · cs.CV · Submitted 2026-09-25 · Read on arXiv

cs.CV

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Embeddings — 17× in this paper · explained in 2 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Semantic Alignment — 6× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Adaptation — 5× in this paper · explained in 1 episode
• Fidelity — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Prompt Engineering — 3× in this paper · explained in 4 episodes
• Saliency Prior Marker Activation — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Attend-to-Anything Model — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Synthetic Data Generation — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Emotional Resonance — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research
• SAM 3: Segment Anything with Concepts
• CogBlender: Towards Continuous Cognitive Intervention in Text-to-Image Generation
• DeepGaze II: Reading fixations from deep features trained on object recognition
• Attend to Anything: Foundation Model for Unified Human Attention Modeling

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models