Convolutional neural networks （CNNs） have been applied in state-of-the-art visual tracking tasks to represent the target. However, most existing algorithms treat visual tracking as an object-specific task. Therefore...Convolutional neural networks （CNNs） have been applied in state-of-the-art visual tracking tasks to represent the target. However, most existing algorithms treat visual tracking as an object-specific task. Therefore, the model needs to be retrained for different test video sequences. We propose a branch-activated multi-domain convolutional neural network （BAMDCNN）. In contrast to most existing trackers based on CNNs which require frequent online training, BAMDCNN only needs offine training and online fine-tuning. Specifically, BAMDCNN exploits category-specific features that are more robust against variations. To allow for learning category-specific information, we introduce a group algorithm and a branch activation method. Experimental results on challenging benchmark show that the proposed algorithm outperforms other state-of-the-art methods. What＇s more, compared with CNN based trackers, BAMDCNN increases tracking speed.展开更多
目的:探讨行为激活团体心理治疗改善非典型抑郁症青少年的脑网络特征的效果。方法:纳入2023年7月至2024年7月于我院诊断的非典型抑郁症青少年120例。通过双重差分模型(difference in difference,DID)验证治疗的净效果;分析脑网络拓扑指...目的:探讨行为激活团体心理治疗改善非典型抑郁症青少年的脑网络特征的效果。方法:纳入2023年7月至2024年7月于我院诊断的非典型抑郁症青少年120例。通过双重差分模型(difference in difference,DID)验证治疗的净效果;分析脑网络拓扑指标与生活质量评分、抑郁评分的关联。结果:DID分析显示治疗后研究组的生活质量综合评定问卷(generic quality of life inventory-74,GQOL-74)评分上升幅度及17项汉密尔顿抑郁量表(Hamilton depression scale-17 item,HAMD-17)评分下降幅度均优于对照组(P均<0.001);治疗后对照组脑网络拓扑指标显著高于研究组(P<0.05);不同脑网络拓扑指标对HAMD-17、GQOL-74评分均存在不同程度影响。结论:行为激活团体心理治疗能够显著改善非典型抑郁症青少年的抑郁症状,提升其生活质量,并对脑网络特征产生积极影响。同时脑网络结构与患者生活质量和抑郁程度密切相关。展开更多
Learning activities interactions between small groups is a key step in understanding team sports videos.Recent research focusing on team sports videos can be strictly regarded from the perspective of the audience rath...Learning activities interactions between small groups is a key step in understanding team sports videos.Recent research focusing on team sports videos can be strictly regarded from the perspective of the audience rather than the athlete.For team sports videos such as volleyball and basketball videos,there are plenty of intra-team and inter-team relations.In this paper,a new task named Group Scene Graph Generation is introduced to better understand intra-team relations and inter-team relations in sports videos.To tackle this problem,a novel Hierarchical Relation Network is proposed.After all players in a video are finely divided into two teams,the feature of the two teams’activities and interactions will be enhanced by Graph Convolutional Networks,which are finally recognized to generate Group Scene Graph.For evaluation,built on Volleyball dataset with additional 9660 team activity labels,a Volleyball+dataset is proposed.A baseline is set for better comparison and our experimental results demonstrate the effectiveness of our method.Moreover,the idea of our method can be directly utilized in another video-based task,Group Activity Recognition.Experiments show the priority of our method and display the link between the two tasks.Finally,from the athlete’s view,we elaborately present an interpretation that shows how to utilize Group Scene Graph to analyze teams’activities and provide professional gaming suggestions.展开更多
基金the Innovation Action Plan Foundation of Shanghai(No.16511101200)
文摘Convolutional neural networks （CNNs） have been applied in state-of-the-art visual tracking tasks to represent the target. However, most existing algorithms treat visual tracking as an object-specific task. Therefore, the model needs to be retrained for different test video sequences. We propose a branch-activated multi-domain convolutional neural network （BAMDCNN）. In contrast to most existing trackers based on CNNs which require frequent online training, BAMDCNN only needs offine training and online fine-tuning. Specifically, BAMDCNN exploits category-specific features that are more robust against variations. To allow for learning category-specific information, we introduce a group algorithm and a branch activation method. Experimental results on challenging benchmark show that the proposed algorithm outperforms other state-of-the-art methods. What＇s more, compared with CNN based trackers, BAMDCNN increases tracking speed.
文摘目的:探讨行为激活团体心理治疗改善非典型抑郁症青少年的脑网络特征的效果。方法:纳入2023年7月至2024年7月于我院诊断的非典型抑郁症青少年120例。通过双重差分模型(difference in difference,DID)验证治疗的净效果;分析脑网络拓扑指标与生活质量评分、抑郁评分的关联。结果:DID分析显示治疗后研究组的生活质量综合评定问卷(generic quality of life inventory-74,GQOL-74)评分上升幅度及17项汉密尔顿抑郁量表(Hamilton depression scale-17 item,HAMD-17)评分下降幅度均优于对照组(P均<0.001);治疗后对照组脑网络拓扑指标显著高于研究组(P<0.05);不同脑网络拓扑指标对HAMD-17、GQOL-74评分均存在不同程度影响。结论:行为激活团体心理治疗能够显著改善非典型抑郁症青少年的抑郁症状,提升其生活质量,并对脑网络特征产生积极影响。同时脑网络结构与患者生活质量和抑郁程度密切相关。
基金National Natural Science Foundation of China(Grant No.U20B2069)Fundamental Research Funds for the Central Universities.
文摘Learning activities interactions between small groups is a key step in understanding team sports videos.Recent research focusing on team sports videos can be strictly regarded from the perspective of the audience rather than the athlete.For team sports videos such as volleyball and basketball videos,there are plenty of intra-team and inter-team relations.In this paper,a new task named Group Scene Graph Generation is introduced to better understand intra-team relations and inter-team relations in sports videos.To tackle this problem,a novel Hierarchical Relation Network is proposed.After all players in a video are finely divided into two teams,the feature of the two teams’activities and interactions will be enhanced by Graph Convolutional Networks,which are finally recognized to generate Group Scene Graph.For evaluation,built on Volleyball dataset with additional 9660 team activity labels,a Volleyball+dataset is proposed.A baseline is set for better comparison and our experimental results demonstrate the effectiveness of our method.Moreover,the idea of our method can be directly utilized in another video-based task,Group Activity Recognition.Experiments show the priority of our method and display the link between the two tasks.Finally,from the athlete’s view,we elaborately present an interpretation that shows how to utilize Group Scene Graph to analyze teams’activities and provide professional gaming suggestions.
