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Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
Thanks to large-scale labeled training data, deep neural networks (DNNs)...
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Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
Sentiment analysis of user-generated reviews or comments on products and...
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A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Large-scale labeled training datasets have enabled deep neural networks ...
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Emotion-Based End-to-End Matching Between Image and Music in Valence-Arousal Space
Both images and music can convey rich semantics and are widely used to i...
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Rethinking Distributional Matching Based Domain Adaptation
Domain adaptation (DA) is a technique that transfers predictive models t...
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Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey
In many practical applications, it is often difficult and expensive to o...
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An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos
Emotion recognition in user-generated videos plays an important role in ...
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Multi-source Domain Adaptation for Visual Sentiment Classification
Existing domain adaptation methods on visual sentiment classification ty...
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Multi-source Distilling Domain Adaptation
Deep neural networks suffer from performance decay when there is domain ...
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Multi-source Domain Adaptation for Semantic Segmentation
Simulation-to-real domain adaptation for semantic segmentation has been ...
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Sketch-Specific Data Augmentation for Freehand Sketch Recognition
Sketch recognition remains a significant challenge due to the limited tr...
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Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Survey
The wide popularity of digital photography and social networks has gener...
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PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
Existing methods on visual emotion analysis mainly focus on coarse-grain...
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Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
We propose to harness the potential of simulation for the semantic segme...
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A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization
State-of-the-art studies have demonstrated the superiority of joint mode...
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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different d...
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A Novel Domain Adaptation Framework for Medical Image Segmentation
We propose a segmentation framework that uses deep neural networks and i...
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SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
Earlier work demonstrates the promise of deep-learning-based approaches ...
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Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld
Large-scale labeled training datasets have enabled deep neural networks ...
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SqueezeNext: Hardware-Aware Neural Network Design
One of the main barriers for deploying neural networks on embedded syste...
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Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Neural networks rely on convolutions to aggregate spatial information. H...
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