Papers with LVLM training
Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models? (2024.emnlp-main)
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| Challenge: | Large vision-language models (LVLMs) often hallucinate and produce captions that mention concepts that cannot be found in the image. |
| Approach: | They propose to add grounding objectives to captions that explicitly align image regions or objects to text spans to reduce hallucination. |
| Outcome: | The proposed evaluation protocol reduces the amount of hallucination in LVLMs by adding grounding objectives. |
Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference (2025.acl-long)
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| Challenge: | Existing Large Vision-Language Models (LVLMs) learn visual capacity through visual instruction tuning. |
| Approach: | They propose a method for LVLMs to be trained by selective layers tuning . they propose removing non-critical layers outside the visual region . |
| Outcome: | The proposed approach preserves nearly 99% of visual performance and improves textual task results while reducing training time. |