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Found 8 papers, 2 papers with code
Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition
The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences.
Joint Attention-Driven Domain Fusion and Noise-Tolerant Learning for Multi-Source Domain Adaptation
As a study on the efficient usage of data, Multi-source Unsupervised Domain Adaptation transfers knowledge from multiple source domains with labeled data to an unlabeled target domain.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Source Separation of Unknown Numbers of Single-Channel Underwater Acoustic Signals Based on Autoencoders
The separation of single-channel underwater acoustic signals is a challenging problem with practical significance.
Underwater single-channel acoustic signal multitarget recognition using convolutional neural networks
The radiated noise from ships is of great significance to target recognition, and several deep learning methods have been developed for the recognition of underwater acoustic signals.
Sequential convolutional network for behavioral pattern extraction in gait recognition
As a unique and promising biometric, video-based gait recognition has broad applications.
Computational efficient deep neural network with difference attention maps for facial action unit detection
A large number of experimental results show that the proposed CEDNN is obviously better than the traditional deep learning method on DISFA+ and CK+ datasets.
Person image generation with semantic attention network for person re-identification
To address this issue, we propose a novel person pose-guided image generation method, which is called the semantic attention network.
HEU Emotion: A Large-scale Database for Multi-modal Emotion Recognition in the Wild
Existing multimodal emotion databases in the real-world conditions are few and small, with a limited number of subjects and expressed in a single language.
