This research presents STATrack, a novel tracking framework based on the Transformer architecture, aimed at addressing these challenges through three key contributions: (1) the Adaptive Spatio-Temporal Consiste
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...
STAR利用TGConv(一种新颖的基于transformer的图卷积机制)建模图内人群交互。图间的时间依赖性由独立的时间transformer建模。STAR通过在空间和时间transformer之间交错来捕捉复杂的时空交互。为了校正时间预测,以应对消失的行人的长期影响,我们引入了一个可读可写的外部存储模块,该模块由时间transformer持续更新。我们表明,仅...
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。
using a visual backbone pretrained on ImageNet with a randomly initialized transformer, in Section 4.2. We also evaluate a MDETR-equivalent baseline in Section 4.2. \label {eq:objective} \mathcal {L} = \lambda _{\mathcal {L}_1}\mathcal {L}_{\mathcal {L}_1}(\...
The Global Transformer has 2 layers and 6 heads. Workflow: The output feature maps of the CNN backbone go through a conv layer and are flattened to patch tokens. The embedding dimension of all Transformers is set to 768. Positional embeddings are only used in ST. ...
提出Snap Video,扩展 EDM[1]、FIT[2] 作为backbone,(1)joint video-image training,把image作为高频video(2)transformer 架构将时空信息融合,作为一个单一的、压缩的、1D latent 向量,这样可以同时进行 spatio-temporal 计算 Related Work Diffusion-based 的 video generation 模型 Latent-shift: Latent diffusion wit...
本文提出一个使用编码器-解码器transformer结构的单目标跟踪框架。其中,编码器建模目标物体和搜索区域的全局空间-时序特征;解码器学习一个预测目标物体空间位置的query。此方法直接预测目标边界框的角点,不使用任何预定义的锚框,不需要汉宁窗、滑动窗平滑和尺度/宽高比惩罚等后处理步骤,极大简化了现有跟踪pipeline。该跟踪...
The approach reorganizes each input video into bag of patches that is then fed into a vision transformer to achieve robust representation. Specifically, a spatiotemporal dropout operation is proposed to fully explore patch-level spatiotemporal cues and serve as effective data augmentation to further ...
We propose a new transformer-based reconstruction method, VSR-SIM, that uses shifted 3-dimensional window multi-head attention in addition to channel attention mechanism to tackle the problem of video super-resolution (VSR) in SIM. The attention mechanisms are found to capture motion in sequences...