论文标题
从头开始的零射击学习（ZFS）：利用本地构图表示
Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations
论文作者
论文摘要
零射击分类是一项概括任务，在训练过程中看不到目标类别的实例。为了允许测试时间传输，每个类都用语义信息注释，通常以属性或文本说明的形式进行注释。尽管经典的零局学习并不能使用来自其他数据集的信息明确禁止，但是在图像基准上实现最佳绝对性能的方法依赖于从ImageNet上预读的编码器提取的功能。这种方法依赖于监督分类环境中的超优化成像网的参数，纠缠着有关这些参数的适用性以及如何通过有关表示和概括的更基本问题来学习的重要问题。为了删除这些干扰器，我们提出了一个更具挑战性的设置：从头开始（ZFS）的零射击学习，该学习明确禁止在其他数据集中对编码器进行微调。我们对这种设置的分析强调了本地信息的重要性和组成表示。
Zero-shot classification is a generalization task where no instance from the target classes is seen during training. To allow for test-time transfer, each class is annotated with semantic information, commonly in the form of attributes or text descriptions. While classical zero-shot learning does not explicitly forbid using information from other datasets, the approaches that achieve the best absolute performance on image benchmarks rely on features extracted from encoders pretrained on Imagenet. This approach relies on hyper-optimized Imagenet-relevant parameters from the supervised classification setting, entangling important questions about the suitability of those parameters and how they were learned with more fundamental questions about representation learning and generalization. To remove these distractors, we propose a more challenging setting: Zero-Shot Learning from scratch (ZFS), which explicitly forbids the use of encoders fine-tuned on other datasets. Our analysis on this setting highlights the importance of local information, and compositional representations.
