Results 41 to 50 of about 372,115 (318)

High-Resolution Crowd Density Maps Generation With Multi-Scale Fusion Conditional GAN

IEEE Access, 2020
The major challenges for density maps estimation and accurate counting stem from the large-scale variations, serious occlusions, and perspective distortions.
Shaonian Huang, Hongjing Zhou, Yao Liu, Rongyuan Chen   +3 more
doaj   +1 more source

MSLF-Net: A Multi-Scale and Multi-Level Feature Fusion Net for Diabetic Retinopathy Segmentation

Diagnostics, 2022
Diabetic Retinopathy (DR) is a diabetic complication that predisposes patients to visual impairments that could lead to blindness. Lesion segmentation using deep learning algorithms is an effective measure to screen and prevent early DR.
Haitao Yan, Jiexin Xie, Deliang Zhu, Lukuan Jia, Shijie Guo   +4 more
doaj   +1 more source

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

, 2019
Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask R-CNN, DetNet) to alleviate the problem arising from scale variation across ...
Cai, Ling, Chen, Ying, Ling, Haibin, Sheng, Tao, Tang, Zhi, Wang, Yongtao, Zhao, Qijie   +6 more
core   +1 more source

Scene Text Detection Based on Multi-scale Feature Extraction and Bidirectional Feature Fusion

Journal of Harbin University of Science and Technology
Natural scene text detection is a fundamental research work in the field of image processing and has a wide range of applications. Currently, natural scene text detection usually adopts single-scale convolution and multi-scale feature fusion to ...
LIAN Zhe, YIN Yanjun, ZHI Min, XU Qiaozhi   +3 more
doaj   +1 more source

Multi-scale Feature Fusion Enhancement for Underwater Object Detection

Sensors
Abstract Underwater object detection (UOD) is crucial for marine ecosystem protection, ocean resource exploration, and maritime security. While generic object detection methods have shown success in atmospheric conditions, they fall short in the underwater environment.
Zhanhao Xiao, Zhenpeng Li, Huihui Li, Mengting Li, Xiaoyong Liu, Yinying Kong   +5 more
openaire   +3 more sources

MSDSANet: Multimodal Emotion Recognition Based on Multi-Stream Network and Dual-Scale Attention Network Feature Representation

Sensors
Aiming at the shortcomings of EEG emotion recognition models in feature representation granularity and spatiotemporal dependence modeling, a multimodal emotion recognition model integrating multi-scale feature representation and attention mechanism is ...
Weitong Sun, Xingya Yan, Yuping Su, Gaihua Wang, Yumei Zhang   +4 more
doaj   +1 more source

An Enhanced Deep Feature Representation for Person Re-identification

, 2016
Feature representation and metric learning are two critical components in person re-identification models. In this paper, we focus on the feature representation and claim that hand-crafted histogram features can be complementary to Convolutional Neural ...
Chen, Ying-Cong, Li, Xiang, Wu, An-Cong, Wu, Shangxuan, You, Jin-Jie, Zheng, Wei-Shi   +5 more
core   +1 more source

MUFFIN: multi-scale feature fusion for drug–drug interaction prediction

Bioinformatics, 2021
Abstract Motivation Adverse drug–drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients and the society.
Yujie Chen, Tengfei Ma, Xixi Yang, Jianmin Wang, Bosheng Song, Xiangxiang Zeng   +5 more
openaire   +2 more sources

Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation

, 2018
Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation ...
Chang, Shih-Fu, Lu, Zhiwu, Niu, Yulei, Wen, Ji-Rong, Xiang, Tao   +4 more
core   +1 more source

GFF: Gated Fully Fusion for Semantic Segmentation

, 2020
Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation ...
Han, Lei, Li, Xiangtai, Tan, Shaohua, Tong, Yunhai, Yang, Kuiyuan, Zhao, Houlong   +5 more
core   +1 more source