We propose a novel deep learning model named ACLNet, for cloud segmentation
from ground images. ACLNet uses both deep neural network and machine learning
(ML) algorithm to extract complementary features. Specifically, it uses
EfficientNet-B0 as the backbone, "`a trous spatial pyramid pooling" (ASPP) to
learn at multiple receptive fields, and "global attention module" (GAM) to
extract finegrained details from the image. ACLNet also uses k-means clustering
to extract cloud boundaries more precisely. ACLNet is effective for both
daytime and nighttime images. It provides lower error rate, higher recall and
higher F1-score than state-of-art cloud segmentation models. The source-code of
ACLNet is available here: this https URL.

ACLNet 是一种有效的云分割模型，利用深度神经网络和机器学习算法提取互补特征，使用 EfficientNet-B0 作为骨干网络，ASPP 学习多个感受野，GAM 提取图像中的细节，并使用 k-means 聚类更精确地提取云边界。其错误率更低，召回率和 F1 - 分数更高，适用于白天和黑夜图像。

ACLNet: 基于注意力和聚类方法的云分割网络

ACLNet: An Attention and Clustering-based Cloud Segmentation Network

We are witnessing a confluence of vision, speech and dialog system
technologies that are enabling the IVAs to learn audio-visual groundings of
utterances and have conversations with users about the objects, activities and
events surrounding them. Recent progress in visual grounding techniques and
Audio Understanding are enabling machines to understand shared semantic
concepts and listen to the various sensory events in the environment. With
audio and visual grounding methods, end-to-end multimodal SDS are trained to
meaningfully communicate with us in natural language about the real dynamic
audio-visual sensory world around us. In this work, we explore the role of
`topics' as the context of the conversation along with multimodal attention
into such an end-to-end audio-visual scene-aware dialog system architecture. We
also incorporate an end-to-end audio classification ConvNet, AclNet, into our
models. We develop and test our approaches on the Audio Visual Scene-Aware
Dialog (AVSD) dataset released as a part of the DSTC7. We present the analysis
of our experiments and show that some of our model variations outperform the
baseline system released for AVSD.

本论文探讨了以话题作为对话背景，利用多模态注意力和音视频定位技术的方法来构建端到端的自然语言对话系统，结合使用 end-to-end 音频分类卷积神经网络 AclNet，以音视频场景感知任务数据集 AVSD 进行测试，并提出了改进方案算法，优于现有基线系统。