搜索区域和初始模版区域分别输入共同的骨架网络,这里使用ResNet-50或 ResNet-101,之后将获得的两个特征图拉长,组合起来输入 Transformer 模块。可以看下这块整体结构的代码: defforward_pass(self,data,run_box_head,run_cls_head):feat_dict_list=[]# process the templatesforiinrange(self.settings.num_template...
1)Input. 两帧模板和一帧搜索区域展平相连接为(H1×W1+H11×W11+H2×W2)大小的向量; 前传函数:网络结构主要包含backbone和transformer两部分 backbone部分处理:输入带有mask的图像patch,输出提取的特征图和位置编码 backbone部分输出为一个包含"feat","mask"和"pos"的字典 transformer部分处理 预测头的处理 初始化tr...
Learning Spatio-Temporal Transformer for Visual Tracking 论文 代码 搜索区域(Search Region):这是图像中的一块区域,通常大于或等于目标的实际大小。搜索区域为模型提供了足够的上下文来识别和定位目标。 初始模板(Initial Template):这是目标在序列开始时的一个参考图像或框,模型使用它来识别后续帧中的相同目标。
Spatio-temporal transformer networkSpatio-temporal flowSpatio-temporal samplerVideo super-resolutionVideo deblurringState-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information. However, these networks typically consider only a pair of consecutive frames ...
在这篇文章中,我们提出了一个新的基于变压器的架构,用于三维人体运动的生成建模任务。以前的工作通常依赖于基于RNN的模型,考虑到较短的预测层位很快达到一个稳定且通常不可信的状态。相反,我们的重点在于在更长的时间范围内产生可信的未来发展。为了缓解向静态姿态收敛
Learning Spatio-Temporal Transformer for Visual Tracking Bin Yan1,∗, Houwen Peng2,†, Jianlong Fu2, Dong Wang1,†, Huchuan Lu1 1Dalian University of Technology 2Microsoft Research Asia Abstract In this paper, we present a new tracking architecture with an encoder-decoder t...
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution Zhongwei Qiu, Huan Yang, Jianlong Fu, Dongmei Fu ECCV 2022|October 2022
Focus on this problem, A novel spatio-temporal tuples Transformer (STTFormer) method is proposed. The skeleton sequence is divided into several parts, and several consecutive frames contained in each part are encoded. And then a spatio-temporal tuples self-attention module is proposed to capture...
PubDate: Jun 2020Teams: ETH Zurich; Peking UniversityWriters: Emre Aksan, Peng Cao, Manuel Kaufmann, Otmar HilligesPDF: A Spatio-temporal Transformer for 3D Human Motion Prediction Project: A Spatio-temporal Transformer for 3D Human Motion Prediction Abs
3.5 Spatio-Temporal Graph Transformer 时间transformer可以单独模拟每个行人的运动动力学,但不能考虑空间交互作用;spatial Transformer利用TGConv处理人群交互,但很难推广到时间序列。行人预测的一个主要挑战是建模耦合时空交互作用。行人的空间和时间动态密切相关。例如,当一个人决定她的下一个动作时,她首先会预测她的...