Papers by Linli Yao
LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation? (2024.naacl-long)
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| Challenge: | Existing models for text-to-image generation have been underperforming in image-totext generation tasks. |
| Approach: | They propose a framework that uses a split BERT to create a dedicated latent space for captions and integrates a regularization module to manage varying text lengths. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the MS COCO dataset with 38.2 BLEU@4 and 126.2 CIDEr . |
Generative Frame Sampler for Long Video Understanding (2025.findings-acl)
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| Challenge: | Existing video large language models (LMMs) employ an impedance of thousands of frames to understand long videos. |
| Approach: | They propose a plug-and-play module integrated with VideoLLMs to facilitate efficient lengthy video perception. |
| Outcome: | The proposed module boosts the performance of open-source VideoLLMs and proprietary assistants on long-form video benchmarks. |
RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction (2025.emnlp-main)
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Yuchi Wang, Yishuo Cai, Shuhuai Ren, Sihan Yang, Linli Yao, Yuanxin Liu, Yuanxing Zhang, Pengfei Wan, Xu Sun
| Challenge: | Existing recaptioning methods suffer from inaccuracies due to missing fine-grained details. |
| Approach: | They propose a framework that refines captions through visual reconstruction using a text-to-image model and a visual reconstruction framework. |
| Outcome: | The proposed framework outperforms baselines on CapsBench and CompreCap by 10%. |