一般项目：计算机软硬件及外围设备制造；数字家庭产品制造；智能家庭网关制造；通信设备制造；网络设备制造；软件开发；网络与信息安全软件开发；软件外包服务；人工智能理论与算法软件开发；人工智能通用应用系统；人工智能应用软件开发；物联网技术研发；信息系统集成服务；智能控制系统集成；人工智能行业应用系统集成服务；计算机系统服务；信息系统运行维护服务；家用电器安装服务；计算机软硬件及辅助设备零售；软件销售；通讯设备销售；移动通信设备销售；网络设备销售；智能家庭消费设备销售；家用视听设备销售；家用电器销售；人工智能硬件销售；云计算设备销售；可穿戴智能设备销售；数字视频监控系统销售；互联网销售（除销售需要许可的商品）；移动终端设备销售；信息安全设备销售；物联网设备销售；智能机器人销售；技术服务、技术开发、技术咨询、技术交流、技术转让、技术推广；网络技术服务；互联网数据服务；信息技术咨询服务；人工智能公共服务平台技术咨询服务；人工智能双创服务平台；物联网技术服务；互联网安全服务；计算机及办公设备维修；人工智能基础资源与技术平台；数据处理和存储支持服务；计算机软硬件及辅助设备批发；销售代理；社会经济咨询服务；广告制作；广告设计、代理；广告发布（非广播电台、电视台、报刊出版单位）；第二类医疗器械销售；会议及展览服务；电子产品销售；非居住房地产租赁(除依法须经批准的项目外，凭营业执照依法自主开展经营活动)。许可项目：第一类增值电信业务；第二类增值电信业务；互联网信息服务；货物进出口；技术进出口；网络文化经营；信息网络传播视听节目(依法须经批准的项目，经相关部门批准后方可开展经营活动，具体经营项目以审批结果为准)。
Multimodal lip recognition aims to enhance speech recognition accuracy and robustness by integrating lip movements and speech information,while also aiding specific user groups in communication.However,existing lip-speaking models predominantly focus on English datasets,leaving research on Chinese lip recognition in its nascent stage.Addressing challenges in handling data features across different modalities,integrating these features,and achieving comprehensive fusion of multimodal features,we propose a multimodal split attention fusion audio visual recognition(MSAFVR)model.Through experiments utilizing a Chinese Mandarin lip reading(CMLR)dataset,our model,MSAFVR,demonstrates significant advancements,achieving a remarkable 92.95%accuracy in Chinese lip reading,surpassing state-of-the-art Mandarin lip reading models.
Given the high cost and difficulty of shadow annotation,the shadow detection performance of supervised learning models in high-resolution remote sensing images is severely limited mainly because of the lack of training samples.To solve this problem,this study proposes a shadow detection method that is based on shadow direction prior,which has unique advantages in learning the inherent shadow information contained in unlabeled high-resolution remote sensing images.Through self-supervised learning methods,the advanced semantic information of ground object shadows contained in remote sensing images can be deeply explored,thereby improving the applicability and accuracy of the model.This innovative approach provides a new way to overcome the data limitation problem in shadow detection and offers new possibilities for improving the model's ability to understand complex high-resolution images. This study proposes an innovative shadow detection method for high-resolution remote sensing images that is based on prior knowledge of the shadow direction and explores the effectiveness of shadow direction prior in representing high-level semantic features of object shadows in remote sensing images.The key task in this method is to construct an auxiliary task through shadow direction prior so that the deep neural network can effectively understand the shadow of objects in remote sensing images.Moreover,to further enhance the deep neural network's ability to learn the key features of object shadows,this study designs a direction transformation-independent noise processing mechanism and data enhancement strategy for self-supervised shadow detection,both of which improve the generalization ability of the model. The effectiveness of the proposed method is proven through verification on the Aerial Imagery Dataset for Shadow Detection(AISD).Experimental results show that compared with the traditional Unet benchmark model,the proposed method can successfully learn prior knowledge in unlabeled high-resolution remote sensing images.By leveraging this prior knowledge,the developed method improves the accuracy of the Unet model and demonstrates competitive efficiency and performance in small sample cases.The results of this study highlight the superiority of the proposed method in addressing the challenge of shadow identification in high-resolution remote sensing images,especially in the absence of labeled samples.By fully utilizing prior knowledge,the method successfully extends the performance of the Unet model and provides a promising solution for processing unlabeled high-resolution remote sensing images in practical applications. The qualitative and quantitative results consistently show that using a shadow direction prior method can considerably improve the accuracy of shadow detection.The use of the DTICT strategy helps alleviate the interference caused by shadow-free samples and samples containing shadows in special directions during network training.The data enhancement strategy combined with contrast cropping and color transformation provides the network with diverse training samples,thereby enhancing the pre-trained model's ability to extract object shadow features.The proposed method achieves competitive object shadow detection accuracy by using a small number of labeled samples.
