Papers with visually grounded tasks
LaMI: Augmenting Large Language Models via Late Multi-Image Fusion (2026.acl-short)
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| Challenge: | Large Language Models lack visual grounding on visual reasoning, despite training on text alone. |
| Approach: | They propose a late multi-image fusion method that augments LLMs with test-time visual signals. |
| Outcome: | Using a late multi-image fusion method, the proposed model outperforms LLMs on visual reasoning and matches VLMs in vision-based tasks. |
Symmetrical Visual Contrastive Optimization: Aligning Vision-Language Models with Minimal Contrastive Images (2025.acl-long)
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| Challenge: | Recent studies have shown that Large Vision-Language Models (VLMs) tend to neglect image content and over-rely on language-model priors, resulting in errors in visually grounded tasks and hallucinations. |
| Approach: | They propose a novel finetuning objective that steers the model toward capturing important visual details and aligning them with corresponding text tokens. |
| Outcome: | The proposed method achieves up to 22% reduction in hallucinations and significant gains in vision-centric and general tasks while maintaining or improving the model's general abilities. |