论文标题
暂时的流口罩注意相机陷阱图像中对野生动物的开放式长尾识别
Temporal Flow Mask Attention for Open-Set Long-Tailed Recognition of Wild Animals in Camera-Trap Images
论文作者
论文摘要
相机陷阱，无人观察设备和基于深度学习的图像识别系统在收集和分析野生动植物图像方面的努力大大减少了。但是，通过上述设备收集的数据表现出1）长尾巴和2）开放式分布问题。为了解决开放式长尾识别问题，我们提出了临时流面膜注意网络，该网络包括三个关键的构建块：1）光流模块，2）注意残留模块，3）一个元插入分类器。我们使用光流模块提取顺序帧的时间特征，并使用注意残差块学习信息表示。此外，我们表明，应用元装置技术可以在开放式长尾识别中提高该方法的性能。我们将此方法应用于韩国非军事区（DMZ）数据集。我们进行了广泛的实验以及定量和定性分析，以证明我们的方法有效地解决了开放式长尾识别问题，同时对未知类别具有强大的态度。
Camera traps, unmanned observation devices, and deep learning-based image recognition systems have greatly reduced human effort in collecting and analyzing wildlife images. However, data collected via above apparatus exhibits 1) long-tailed and 2) open-ended distribution problems. To tackle the open-set long-tailed recognition problem, we propose the Temporal Flow Mask Attention Network that comprises three key building blocks: 1) an optical flow module, 2) an attention residual module, and 3) a meta-embedding classifier. We extract temporal features of sequential frames using the optical flow module and learn informative representation using attention residual blocks. Moreover, we show that applying the meta-embedding technique boosts the performance of the method in open-set long-tailed recognition. We apply this method on a Korean Demilitarized Zone (DMZ) dataset. We conduct extensive experiments, and quantitative and qualitative analyses to prove that our method effectively tackles the open-set long-tailed recognition problem while being robust to unknown classes.
