Furthermore, to alleviate over-segmentation errors in action segmentation, we propose to generate more stable and distinguishable features via temporal context aggregation at local scales. Especially,our method, termed as Feature Aggregation Module (FAM), is a general module, and can be integrated ...
This work proposes an optimized Real-Time Detection Transformer (RT-DETR) model for VOD that introduces a decoupled Feature Aggregation Module (FAM) to separately refine the localization and classification detection heads. This method only requires a minimal increase in the number of parameters to ...
基于特征金字塔,在自底向上路径上加入GGM模块(Global Guidance Module),目的是为不同特征层提供潜在显著对象的位置信息。在自顶向下路径上加入FAM模块(Feature Aggregation Module),目的是将粗糙语义信息和细致特征更好融合。在FPNs的融合操作后的自顶向下路径中加入FAMs,能够多尺度......
However, SE is conventionally used in the backbone for enhancing feature extraction, while FSM is used in the neck (i.e. top-down pathway) for enhanc- ing multi-scale feature aggregation. Additionally, the se- lected/scaled features from FSM are also supplied as refer- ences to FAM for ...
GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering Weiqing Yan1,5, Yuanyang Zhang1, Chenlei Lv2, Chang Tang3*, Guanghui Yue4, Liang Liao5, Weisi Lin5 1School of Computer and Control Engineering, Yantai University, Yantai 264005, ...
Then, a pyramids aggregation block (PAB) is devised to transform the pyramids into final detection pyramid. This module is illustrated in the Fig. 4. Figure 4 The structure of pyramids aggregation block. Full size image The PAB consists of two steps. First, the same scale features in ...
A Feature Aggregation Network for Multispectral Pedestrian Detection (FANet) Feature maps generated by our FANet overlapped on the color and thermal images. (a) represents original color and thermal image pairs, (b) and (c) represent feature maps without and with FAM, respectively. Redder color ...
First, a Hierarchical Feature Alignment Module (HFAM) based on multiple kernel maximum mean discrepancy is integrated into each level to reduce domain shift, which achieves feature alignment of heterogeneous BTIs and obtains the difference features through the aligned features. Then, we devise a ...
attentional convolution was trained to capture information about the location of brain regions that were differentially varied across the different classes. The discriminant probability information from the position was denoted asG ∈ Rw×h×d×1. Inspired by the aggregation method, the diagnostic ...
Inspired by the aggregation method, the diagnostic basis was defined by aggregating the characteristics of local pathological regions and the discriminant probability information [42]. Specifi- cally, the element-by-element multiplication of the feature X with discriminative prob- ability information G ...