SAGE: Sink-Aware Guided Emphasis for Visual Grounding in Vision-Language Decoders
cs.CV, cs.CL
Submitted: 2026-10-08
Updated: 2026-10-08
Terminology
Sources
- GPT-4 Technical Report
- Qwen Technical Report
- Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations
- PaLI-X: On Scaling up a Multilingual Vision and Language Model
- PaLI: A Jointly-Scaled Multilingual Language-Image Model
- A Survey of Multimodal Hallucination Evaluation and Detection
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models?
- Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
- Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
- Evaluating Object Hallucination in Large Vision-Language Models
- A Survey on Hallucination in Large Vision-Language Models
- Large Language Models: A Survey
- DINOv3
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- Efficient Large Language Models: A Survey
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
- Efficient Streaming Language Models with Attention Sinks
- Qwen3 Technical Report
- MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
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