Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dyn...Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sign language requires identifying keyframes that best represent the signs, and missing these keyframes reduces accuracy. Secondly, some methods do not focus enough on hand regions, which are small within the overall frame, leading to information loss. To address these challenges, we propose a novel Video Transformer Attention-based Network (VTAN) for dynamic sign language recognition. Our approach prioritizes informative frames and hand regions effectively. To tackle the first issue, we designed a keyframe extraction module enhanced by a convolutional autoencoder, which focuses on selecting information-rich frames and eliminating redundant ones from the video sequences. For the second issue, we developed a soft attention-based transformer module that emphasizes extracting features from hand regions, ensuring that the network pays more attention to hand information within sequences. This dual-focus approach improves effective dynamic sign language recognition by addressing the key challenges of identifying critical frames and emphasizing hand regions. Experimental results on two public benchmark datasets demonstrate the effectiveness of our network, outperforming most of the typical methods in sign language recognition tasks.展开更多
In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinfor...In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.展开更多
In the study on Ca-Mg silicate crystalline glazes, we found some disequilibrated crystallization phenomena, such as non-crystallographic small angle forking and spheroidal growth, parasitism and wedging-form of crysta...In the study on Ca-Mg silicate crystalline glazes, we found some disequilibrated crystallization phenomena, such as non-crystallographic small angle forking and spheroidal growth, parasitism and wedging-form of crystals, dendritic growth, secondary nucleation, etc. Those phenomena possibly resulted from two factors: (1) partial temperature gradient, which is caused by heat asymmetry in the electrical resistance furnace, when crystals crystalize from silicate melt; (2) constitutional supercooling near the surface of crystals. The disparity of disequilibrated crystallization phenomena in different main crystalline phases causes various morphological features of the crystal aggregates. At the same time, disequilibrated crystallization causes great stress retained in the crystals, which results in cracks in glazes when the temperature drops. According to the results, the authors analyzed those phenomena and displayed correlative figures and data.展开更多
车道线检测对智能车辆的环境感知和驾驶决策至关重要,针对现有方法在复杂交通道路场景中难以同时兼顾检测精度与速度的问题,提出一种高效的单目3D车道线检测方法。该方法通过采用动态蛇形卷积,有效提取车道线弯曲和细长形态特征;利用不...车道线检测对智能车辆的环境感知和驾驶决策至关重要,针对现有方法在复杂交通道路场景中难以同时兼顾检测精度与速度的问题,提出一种高效的单目3D车道线检测方法。该方法通过采用动态蛇形卷积,有效提取车道线弯曲和细长形态特征;利用不同尺度的视图变换与特征聚合,减少特征损失,获取不同层次的车道线位置信息;采用残差连接的辅助监督策略,增强模型的表征能力。在3D车道线评测数据集上的实验结果显示,该方法在虚拟仿真数据集Apollo 3D synthetic上的F_(1)-score指标达到98.6%,相较于目前先进的BEV-LaneDet和LATR方法分别提升了1.7%和1.8%;在大规模真实场景OpenLane数据集上的F_(1)-score指标达到59.8%,相较于BEV-LaneDet,提升了1.4%,在上下坡和弯道场景中的精度提升尤为明显,分别为4.6%和2.5%;F_(1)-score虽比基于Transformer的LATR方法低2.1%,但运行速度80.1 fps是后者14 fps的5.7倍。结果表明,该方法能够提高复杂场景下3D车道线检测的准确性和鲁棒性,实现检测性能与运行推理速度之间的较好平衡。展开更多
Detection and counting of abalones is one of key technologies of abalones breeding density estimation.The abalones in the breeding stage are small in size,densely distributed,and occluded between individuals,so the ex...Detection and counting of abalones is one of key technologies of abalones breeding density estimation.The abalones in the breeding stage are small in size,densely distributed,and occluded between individuals,so the existing object detection algorithms have low precision for detecting the abalones in the breeding stage.To solve this problem,a detection and counting method of juvenile abalones based on improved SSD network is proposed in this research.The innovation points of this method are:Firstly,the multi-layer feature dynamic fusion method is proposed to obtain more color and texture information and improve detection precision of juvenile abalones with small size;secondly,the multiscale attention feature extraction method is proposed to highlight shape and edge feature information of juvenile abalones and increase detection precision of juvenile abalones with dense distribution and individual coverage;finally,the loss feedback training method is used to increase the diversity of data and the pixels of juvenile abalones in the images to get the even higher detection precision of juvenile abalones with small size.The experimental results show that the AP@0.5 value,AP@0.7 value and AP@0.75 value of the detection results of the proposed method are 91.14%,89.90% and 80.14%,respectively.The precision and recall rates of the counting results are 99.59% and 97.74%,respectively,which are superior to the counting results of SSD,FSSD,MutualGuide,EfficientDet and VarifocalNet models.The proposed method can provide support for real-time monitoring of aquaculture density for juvenile abalones.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.62076117 and 62166026the Jiangxi Provincial Key Laboratory of Virtual Reality under Grant No.2024SSY03151.
文摘Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sign language requires identifying keyframes that best represent the signs, and missing these keyframes reduces accuracy. Secondly, some methods do not focus enough on hand regions, which are small within the overall frame, leading to information loss. To address these challenges, we propose a novel Video Transformer Attention-based Network (VTAN) for dynamic sign language recognition. Our approach prioritizes informative frames and hand regions effectively. To tackle the first issue, we designed a keyframe extraction module enhanced by a convolutional autoencoder, which focuses on selecting information-rich frames and eliminating redundant ones from the video sequences. For the second issue, we developed a soft attention-based transformer module that emphasizes extracting features from hand regions, ensuring that the network pays more attention to hand information within sequences. This dual-focus approach improves effective dynamic sign language recognition by addressing the key challenges of identifying critical frames and emphasizing hand regions. Experimental results on two public benchmark datasets demonstrate the effectiveness of our network, outperforming most of the typical methods in sign language recognition tasks.
文摘In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.
基金Supported by the Natural Science Foundation of Fujian Province(No.D0 2 10 0 12 )
文摘In the study on Ca-Mg silicate crystalline glazes, we found some disequilibrated crystallization phenomena, such as non-crystallographic small angle forking and spheroidal growth, parasitism and wedging-form of crystals, dendritic growth, secondary nucleation, etc. Those phenomena possibly resulted from two factors: (1) partial temperature gradient, which is caused by heat asymmetry in the electrical resistance furnace, when crystals crystalize from silicate melt; (2) constitutional supercooling near the surface of crystals. The disparity of disequilibrated crystallization phenomena in different main crystalline phases causes various morphological features of the crystal aggregates. At the same time, disequilibrated crystallization causes great stress retained in the crystals, which results in cracks in glazes when the temperature drops. According to the results, the authors analyzed those phenomena and displayed correlative figures and data.
文摘车道线检测对智能车辆的环境感知和驾驶决策至关重要,针对现有方法在复杂交通道路场景中难以同时兼顾检测精度与速度的问题,提出一种高效的单目3D车道线检测方法。该方法通过采用动态蛇形卷积,有效提取车道线弯曲和细长形态特征;利用不同尺度的视图变换与特征聚合,减少特征损失,获取不同层次的车道线位置信息;采用残差连接的辅助监督策略,增强模型的表征能力。在3D车道线评测数据集上的实验结果显示,该方法在虚拟仿真数据集Apollo 3D synthetic上的F_(1)-score指标达到98.6%,相较于目前先进的BEV-LaneDet和LATR方法分别提升了1.7%和1.8%;在大规模真实场景OpenLane数据集上的F_(1)-score指标达到59.8%,相较于BEV-LaneDet,提升了1.4%,在上下坡和弯道场景中的精度提升尤为明显,分别为4.6%和2.5%;F_(1)-score虽比基于Transformer的LATR方法低2.1%,但运行速度80.1 fps是后者14 fps的5.7倍。结果表明,该方法能够提高复杂场景下3D车道线检测的准确性和鲁棒性,实现检测性能与运行推理速度之间的较好平衡。
基金jointly supported by the National Key R&D Project(2020YFD0900204)the Yantai Key R&D Project(2019XDHZ084).
文摘Detection and counting of abalones is one of key technologies of abalones breeding density estimation.The abalones in the breeding stage are small in size,densely distributed,and occluded between individuals,so the existing object detection algorithms have low precision for detecting the abalones in the breeding stage.To solve this problem,a detection and counting method of juvenile abalones based on improved SSD network is proposed in this research.The innovation points of this method are:Firstly,the multi-layer feature dynamic fusion method is proposed to obtain more color and texture information and improve detection precision of juvenile abalones with small size;secondly,the multiscale attention feature extraction method is proposed to highlight shape and edge feature information of juvenile abalones and increase detection precision of juvenile abalones with dense distribution and individual coverage;finally,the loss feedback training method is used to increase the diversity of data and the pixels of juvenile abalones in the images to get the even higher detection precision of juvenile abalones with small size.The experimental results show that the AP@0.5 value,AP@0.7 value and AP@0.75 value of the detection results of the proposed method are 91.14%,89.90% and 80.14%,respectively.The precision and recall rates of the counting results are 99.59% and 97.74%,respectively,which are superior to the counting results of SSD,FSSD,MutualGuide,EfficientDet and VarifocalNet models.The proposed method can provide support for real-time monitoring of aquaculture density for juvenile abalones.
