1 citations · 1 across the 11 of their papers we have counts for
11 papers
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory
Mingzhuo Li, Guang Li, Jiafeng Mao +2
Dataset distillation enables the training of deep neural networks with comparable performance in significantly reduced time by compressing large datasets into small and representat…
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…
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…