47 citations · 154 across the 34 of their papers we have counts for
11 papers · 1 filter
LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation
Keigo Sakurai, Ren Togo, Takahiro Ogawa +1
Knowledge Graphs (KGs) represent relationships between entities in a graph structure and have been widely studied as promising tools for realizing recommendations that consider the…
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering
He Zhu, Ren Togo, Takahiro Ogawa +1
Conventional medical artificial intelligence (AI) models face barriers in clinical application and ethical issues owing to their inability to handle the privacy-sensitive character…
MMT-BERT: Chord-aware Symbolic Music Generation Based on Multitrack Music Transformer and MusicBERT
Jinlong Zhu, Keigo Sakurai, Ren Togo +2
We propose a novel symbolic music representation and Generative Adversarial Network (GAN) framework specially designed for symbolic multitrack music generation. The main theme of s…
Cross-domain Multi-step Thinking: Zero-shot Fine-grained Traffic Sign Recognition in the Wild
Yaozong Gan, Guang Li, Ren Togo +3
In this study, we propose Cross-domain Multi-step Thinking (CdMT) to improve zero-shot fine-grained traffic sign recognition (TSR) performance in the wild. Zero-shot fine-grained T…
Generative Dataset Distillation Based on Diffusion Model
Duo Su, Junjie Hou, Guang Li +4
This paper presents our method for the generative track of The First Dataset Distillation Challenge at ECCV 2024. Since the diffusion model has become the mainstay of generative mo…
Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition
Yaozong Gan, Guang Li, Ren Togo +3
Recent multimodal large language models (MLLM) such as GPT-4o and GPT-4v have shown great potential in autonomous driving. In this paper, we propose a cross-domain few-shot in-cont…
Zero-shot Composed Image Retrieval Considering Query-target Relationship Leveraging Masked Image-text Pairs
Huaying Zhang, Rintaro Yanagi, Ren Togo +2
This paper proposes a novel zero-shot composed image retrieval (CIR) method considering the query-target relationship by masked image-text pairs. The objective of CIR is to retriev…
Reinforcing Pre-trained Models Using Counterfactual Images
Xiang Li, Ren Togo, Keisuke Maeda +2
This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. Deep learning classification models are often traine…
Generative Dataset Distillation: Balancing Global Structure and Local Details
Longzhen Li, Guang Li, Ren Togo +3
In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a…
Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach
Taro Togo, Ren Togo, Keisuke Maeda +2
This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism, aimed at dynamically managing class information for be…
Prompt-based Personalized Federated Learning for Medical Visual Question Answering
He Zhu, Ren Togo, Takahiro Ogawa +1
We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns in traditional medical visual question answering (VQ…