Multi-scale large receptive field feature distillation network for lightweight infrared image super-resolutionInfrared imaging plays a pivotal role in applications such as remote sensing, unmanned aerial vehicle
First, the multi-scale receptive field (MSRF) is introduced to work with the pre-trained model, Inception-V4 [14], to extract multi-scale features. As discussed above, due to the intra-class variations and inter-class similarities, simple feature descriptors cannot represent the hot rolled ...
Face detection remains a challenging problem due to the high variability of scale and occlusion despite the strong representational power of deep convolutional neural networks and their implicit robustness. To handle hard face detection under extreme circumstances especially tiny faces detection, in this ...
In the future, we consider integrating the automatic detection and segmentation of COVID-19, and conduct research on the automatic diagnosis system of COVID-19.Similar content being viewed by others Multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation from...
A top-down horizontal connection structure is used for multi-scale fusion. (e) MRFENet. Our proposed MRFENet uses the dilated bottleneck as the base unit to expand the receptive field and obtains features that facilitate small object detection. In this figure, the detection head network is ...
The main structure of our proposed Multi-Scale Coupled Attention network. Full size image A Multi-Scale Coupled Attention (MSCA) network is developed for object detection. Accordingly, it consists of a Multi-Scale Coupled Channel Attention (MSCCA) module, and a Multi-Scale Coupled Spatial Attentio...
However, most existing face restoration models omit the multiple scale issues in the face restoration problem, which is still not well solved in the research area. In this paper, we propose a sequential gating ensemble network (SGEN) for a multiscale noise robust face restoration issue. To ...
In addition, the model includes a multiscale convolution layer and a residual network. The ReLU function is chosen to activate the detection network because tamper detection is prone to gradient disappearance during the training process. The maximum pool layer, which differs from the average pool ...
代码地址:GitHub - dvlab-research/MSAD: Multi-Scale Aligned Distillation for Low-Resolution Detection (CVPR2021) 一、要解决的问题(Why) 这篇文章做的任务是目标检测。一般来说,降低图像分辨率可以显著提高目标检测的精度,但与此同时精度也会降低。本文的目标和 [CVPR2020]Dual Super-Resolution Learning for ...
Furthermore, graph edges are introduced into Graph Attention Network (GAT) to acquire the local semantic feature of the graph. Moreover, inspired by the transformer network, we design a novel multi-scale receptive field GAT to extract the local-global adjacent node-features and edges-features. ...