47 citations · 158 across the 35 of their papers we have counts for
6 papers · 1 filter
Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence
Wenbo Huang, Jinghui Zhang, Guang Li +6
In few-shot action recognition (FSAR), long sub-sequences of video naturally express entire actions more effectively. However, the high computational complexity of mainstream Trans…
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…
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…
Importance-Aware Adaptive Dataset Distillation
Guang Li, Ren Togo, Takahiro Ogawa +1
Herein, we propose a novel dataset distillation method for constructing small informative datasets that preserve the information of the large original datasets. The development of…