Context-aware feature aggregation model adopts dilated convolutions to extract high-level features, yet mitigates potential harm to small targets by clustering local features and reducing the dilation factor. Subsequently, abstract and detailed information are effectively fused through a cross-layer feature...
模型骨架网络采用 ResNet-50 作为编码器,之后作者提出由多个模块组合的解码器用来整合各种特征。首先,对于高层次和低层次特征的聚合,作者提出了特征交织聚合模块(FIAM: Feature Interweaved Aggregation Module),具体结构如下: 首先,低层次的特征先经过一个 1*1 的卷积压缩其特征,之后高层次特征分支经过或不经过卷积与...
Feature Interweaved Aggregation Benefits Combine low-level features and high-level features. 取长补短 Additionally use global context information to help understand the relationship between different objects (ping-pong ball for example), which is beneficial in generate more complete and accurate saliency ...
The proposed method addresses this challenge byintroducing a novel Context-aware Feature Aggregation (CFA) module. The CFA module isdesigned to capture both local and global information by aggregating features of the networkin latent space. This allows the method to better understand the contextual ...
dimension. decoder:由卷积和上采样操作组成,是一个有效的上采样模块,将low-level特征和high-level特征融合。1、the lightweight backbone FC... sub-networkaggregation:将之前backbone的high-levelfeature上采样然后再输入到下一个backbone中refine预测结果,可看做是:a ...
Representation Aggregation for Context Learning 局部特征已经由LR-CNN学习。因此,与从原始图像进行上下文学习相比,从特征立方体学习空间上下文的任务相对来说会比较容易。三个context block的输出接着一个global average pooling, a fc, a softmax layer to make the final prediction in the required number of classe...
Pre-processing and aggregation of sensors' data can provide information that is more complex. In context-aware healthcare monitoring scenarios, there are interesting factors that could be measured, such as: • User's profile(s). Name, gender, age, social and health situation or patient ...
Therefore, we constitute a novel 3D object detection with Context-aware and dimensional Interaction Attention Network (CIANet) to explore vital geometric cues for enriching the feature representation of the object, thus boosting the overall detection performance. Specifically, in the first stage, we ...
feature of novel object can be easily overlooked with only a few data available. In this work, aiming to fully exploit features of annotated novel object and capture fine-grained features of query object, we propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to tackle the...
This aggregation used a local multi-head mask on the atoms that constitute each residue (S = 64, Nhead = 4). Finally, we employed a multi-layer perceptron in the last module, which used three layers of hidden size (S = 64) to decode the state of all ...