Papers with computer vision techniques
Towards LLMs Robustness to Changes in Prompt Format Styles (2025.naacl-srw)
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| Challenge: | Existing prompt engineering techniques for adapting pre-trained LLMs to specific tasks are zero-shot prompting and few-shot supervised fine-tuning. |
| Approach: | They propose a technique for addressing prompt brittleness by diversifying the styles used in the prompt few-shot examples by using computer vision techniques. |
| Outcome: | Empirical results show that the proposed technique reduces style-induced prompt brittleness while improving overall performance across prompt variations and different datasets. |
LiViTo: Linguistic and Visual Features Tool for Assisted Analysis of Historic Manuscripts (2020.lrec-1)
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| Challenge: | a mixed methods approach is feasible for the identification of scribes and authors in handwritten documents. |
| Approach: | They propose a mixed methods approach to the identification of scribes and authors in handwritten documents . they use a software tool which combines linguistic insights and computer vision techniques . |
| Outcome: | The proposed tool can be used to identify scribes and authors in handwritten documents. |
Exploring Fine-Grained Human Motion Video Captioning (2025.coling-main)
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Bingchan Zhao, Xinyi Liu, Zhuocheng Yu, Tongchen Yang, Yifan Song, Mingyu Jin, Sujian Li, Yizhou Wang
| Challenge: | Existing video captioning models fail to capture nuanced semantics of videos . existing models generate coarse descriptions of human motions, resulting in poor quality . |
| Approach: | They construct a fine-grained human motion video captioning dataset named BoFiT and a model that generates fine-grain descriptions of human motions via prompting. |
| Outcome: | The proposed model outperforms existing models on comprehensive metrics. |