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Found 15 papers, 8 papers with code
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention
The recent advances of compressing high-accuracy convolution neural networks (CNNs) have witnessed remarkable progress for real-time object detection.
Ranked #13 on
Object Detection
on PASCAL VOC 2007
DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary
Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years.
DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention
Object detection often costs a considerable amount of computation to get satisfied performance, which is unfriendly to be deployed in edge devices.
Universal Multi-Source Domain Adaptation
Recent study reveals that knowledge can be transferred from one source domain to another unknown target domain, called Universal Domain Adaptation (UDA).
Unveiling Class-Labeling Structure for Universal Domain Adaptation
As a more practical setting for unsupervised domain adaptation, Universal Domain Adaptation (UDA) is recently introduced, where the target label set is unknown.
Branch-Cooperative OSNet for Person Re-Identification
Multi-branch is extensively studied for learning rich feature representation for person re-identification (Re-ID).
Metric-Learning-Assisted Domain Adaptation
Domain alignment (DA) has been widely used in unsupervised domain adaptation.
BiCANet: Bi-directional Contextual Aggregating Network for Image Semantic Segmentation
Exploring contextual information in convolution neural networks (CNNs) has gained substantial attention in recent years for semantic segmentation.
Diversity-Achieving Slow-DropBlock Network for Person Re-Identification
In particular, we show that the feature diversity can be well achieved with the use of multiple dropping branches by setting individual dropping ratio for each branch.
Entropy Minimization vs. Diversity Maximization for Domain Adaptation
Entropy minimization has been widely used in unsupervised domain adaptation (UDA).
Ranked #6 on
Domain Adaptation
on ImageCLEF-DA
Learning Diverse Features with Part-Level Resolution for Person Re-Identification
Learning diverse features is key to the success of person re-identification.
Ranked #3 on
Person Re-Identification
on Market-1501-C
FDDWNet: A Lightweight Convolutional Neural Network for Real-time Sementic Segmentation
The comprehensive experiments demonstrate that our model achieves state-of-the-art results in terms of available speed and accuracy trade-off on CityScapes and CamVid datasets.
ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation
The whole network has nearly symmetric architecture, which is mainly composed of a series of factorized convolution unit (FCU) and its parallel counterparts (PFCU).
Ranked #28 on
Real-Time Semantic Segmentation
on Cityscapes test
LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation
Ranked #29 on
Real-Time Semantic Segmentation
on Cityscapes test
Fast Dynamic Routing Based on Weighted Kernel Density Estimation
Capsules as well as dynamic routing between them are most recently proposed structures for deep neural networks.
Ranked #5 on
Image Classification
on smallNORB
