1 citations · 1 across the 17 of their papers we have counts for
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Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation
Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa +1
Dimensionality reduction (DR) offers a useful representation of complex high-dimensional data. Recent DR methods focus on hyperbolic geometry to derive a faithful low-dimensional r…
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…
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…
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…