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世界最高峰の信号処理に関する国際会議 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024: https://2024.ieeeicassp.org/)にて、研究室より9件の発表を行いました！

1. T. Seino, N. Saito, T. Ogawa, S. Asamizu and M. Haseyama, “Confidence-Aware Spatial-Temporal Attention Graph Convolutional Network for Skeleton-Based Expert-Novice Level Classification”
2. H. Zhu, R. Togo, T. Ogawa and M. Haseyama, “Prompt-Based Personalized Federated Learning for Medical Visual Question Answering”
3. M. Kashiwagi, K. Maeda, R. Togo, T. Ogawa and M. Haseyama, “Enhancing Noisy Label Learning Via Unsupervised Contrastive Loss with Label Correction Based on Prior Knowledge”
4. M. Sato, K. Maeda, R. Togo, T. Ogawa and M. Haseyama, “Caption Unification for Multi-View Lifelogging Images Based on In-Context Learning with Heterogeneous Semantic Contents”
5. Y. Feng, K. Maeda, T. Ogawa and M. Haseyama, “Privacy Preserving Gaze Estimation Via Federated Learning Adapted To Egocentric Video”
6. Y. Watanabe, R. Togo, K. Maeda, T. Ogawa and M. Haseyama, “TolerantGAN: Text-Guided Image Manipulation Tolerant to Real-World Image”
7. Z. Li, R. Togo, T. Ogawa and M. Haseyama, “Source-Data-Free Cross-Domain Knowledge Transfer for Semantic Segmentation”
8. Y. Moroto, Y. Ye, K. Maeda, T. Ogawa and M. Haseyama, “Zero-Shot Visual Sentiment Prediction via Cross-Domain Knowledge Distillation”
9. H. Matsuda, R. Togo, K. Maeda, T. Ogawa and M. Haseyama, “Multi-Object Editing in Personalized Text-To-Image Diffusion Model Via Segmentation Guidance”