Deep Semantic Structural Constraints for Zero-Shot Learning | |
Li, Yan; Jia, Zhen; Zhang, Junge; Huang, Kaiqi; Tan, Tieniu | |
2018 | |
会议名称 | American Association for AI National Conference (AAAI) |
会议日期 | 2018.2.2-2018.2.7 |
会议地点 | New Orleans, Louisiana, USA |
摘要 | Zero-shot learning aims to classify unseen image categories by learning a visual-semantic embedding space. In most cases, the traditional methods adopt a separated two-step pipeline that extracts image features from pre-trained CNN models. Then the fixed image features are utilized to learn the embedding space. It leads to the lack of specific structural semantic information of image features for zero-shot learning task. In this paper, we propose an end-to-end trainable Deep Semantic Structural Constraints model to address this issue. The proposed model contains the Image Feature Structure constraint and the Semantic Embedding Structure constraint, which aim to learn structure-preserving image features and endue the learned embedding space with stronger generalization ability respectively. With the assistance of semantic structural information, the model gains more auxiliary clues for zero-shot learning. The state-of-the-art performance certifies the effectiveness of our proposed method. |
收录类别 | EI |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 小样本高噪声数据学习 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/19687 |
专题 | 智能感知与计算研究中心 |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li, Yan,Jia, Zhen,Zhang, Junge,et al. Deep Semantic Structural Constraints for Zero-Shot Learning[C],2018. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
zsl-aaai-CR.pdf(1739KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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