Papers with visual-textual alignment
Granularity Matters: Pathological Graph-driven Cross-modal Alignment for Brain CT Report Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for automatic Brain CT reports are limited by coarse-grained supervision and coupled cross-modal alignment. |
| Approach: | They propose a pathological Graph-driven cross-modal alignment model that learns fine-grained visual cues and aligns them with textual words. |
| Outcome: | The proposed model can improve the automatic generation of Brain CT reports and contribute to improved cranial disease diagnosis. |
VLA-Mark: A cross modal watermark for large vision-language alignment models (2025.emnlp-main)
Copied to clipboard
Shuliang Liu, Zheng Qi, Jesse Jiaxi Xu, Yibo Yan, Junyan Zhang, He Geng, Aiwei Liu, Peijie Jiang, Jia Liu, Yik-Cheung Tam, Xuming Hu
| Challenge: | Existing text watermarking methods disrupt visual-textual alignment, leaving semantic-critical concepts vulnerable. |
| Approach: | They propose a vision-aligned framework that embeds detectable watermarks into outputs . they combine localized patch affinity, global semantic coherence, contextual attention patterns . |
| Outcome: | The proposed framework shows lower PPL and higher BLEU than conventional methods with near-perfect detection (98.8% AUC). |