Loss function也非常直观,首先是partial feature branch部分的loss,上面提到了每一个part都会预测一个id,因此这里的id loss是cross entropy的和:yi是每个part的id预测结果,y是ground truth,CE代表cross entropy。 而对于pose guided branch来说,loss同样直观,通过最后的feature vector会预测一个id,因此这里还是id loss...
Current frameworks for oriented detection modules are constrained by intrinsic limitations, including excessive computational and memory overheads, discrepancies between predefined anchors and ground truth bounding boxes, intricate training processes, and feature alignment inconsistencies. To overcome these ...
基于这个想法,论文中设计了两个Alignment Loss用来对齐item id的embdding和特征分离表征这两部分信息,loss的定义如公式(5): \begin{align} \mathop{\boldsymbol{v}}\limits^{\sim}&=\frac{1}{A}\sum_{a=1}^{A}{\boldsymbol{v}^a}\\ \mathcal{L}_{MSE}^{A} &= \frac{1}{2}||\textbf{e}...
However, as shown in Fig. 1, adaptation in this manner alone cannot effectively learn a common feature space for the classification in the two domains. This claim is empirically validated in Section 5.1. To address this problem, we propose adiscriminative feature alignment(DFA) to align the two...
Center-aware Feature Alignment 的主要作用是什么? 在领域自适应中如何实现 Center-aware Feature Alignment ? 这种技术对图像处理有何特殊意义? 摘要 域适配目标检测旨在将目标检测器适配到未知的域,新的域可能会遇到各种各样的外观变化,包括外观,视角或者背景。现存的大多数方法在图像级或者实例级上采用图像对齐的方...
pythonpytorchobject-detectionattention-mechanismsodaliasingfeature-fusionsmall-object-detectionfeature-alignment UpdatedDec 21, 2023 Python Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering graphvisual-question-answeringfeature-fusiongraph-attention-networkgraph-matcing...
Pose-Guided Feature Alignment for Occluded Person Re-Identification阅读笔记,程序员大本营,技术文章内容聚合第一站。
论文阅读笔记之RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment,程序员大本营,技术文章内容聚合第一站。
anchor free分支的总损失是图像中所有非忽略区域的focal loss的和,同时通过有效区域内的像素个数来进行正则化处理。 框回归的输出 回归输出的ground truth是与类别无关的4个offset maps,实例只作用于offeset maps上的有效区域 ,对于该区域内的每个像素,用一个四维的向量表示映射框。
之后使用reconstruction generator Gr 来保证生成的target-like image 能保留source domain image 中的结构信息。 Gr 使用cycle consistency loss进行优化:(2)Lcyc=Exs∼IS[||xs→t→s−xs||1 (3)L1=LGAN+λ1Lcyc Content and Style Feature Alignment ...