| 2 | Support Vector Machine active learning for 3D model retrieval显示文摘In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user's semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of | LENG Biao QIN Zheng LI Li-qun | 2007 | Journal of Zhejiang University-Science A(Applied Physics & Engineering)2007，8，12： | 6 |
| 4 | Recent advances of few-shot learning methods and applications显示文摘The rapid development of deep learning provides great convenience for production and life.However,the massive labels required for training models limits further development.Few-shot learning which can obtain a high-performance model by learning few samples in new tasks,providing a solution for many scenarios that lack samples.This paper summarizes few-shot learning algorithms in recent years and proposes a taxonomy.Firstly,we introduce the few-shot learning task and its significance.Secondly,according to different implementation strategies,few-shot learning methods in recent years are divided into five categories,including data augmentation-based methods,metric learning-based methods,parameter optimization-based methods,external memory-based methods,and other approaches.Next,We investigate the application of few-shot learning methods and summarize them from three directions,including computer vision,human-machine language interaction,and robot actions.Finally,we analyze the existing few-shot learning methods by comparing evaluation results on mini Image Net,and summarize the whole paper. | WANG JianYuan LIU KeXin ZHANG YuCheng LENG Biao LU JinHu | 2023 | Science China(Technological Sciences)2023，66，4： | 0 |
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