research
∙
09/13/2023
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection
Emotion detection is a critical technology extensively employed in diver...
share
research
∙
07/25/2023
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion
The emergence of artificial emotional intelligence technology is revolut...
research
∙
10/27/2021
2nd Place Solution for VisDA 2021 Challenge – Universally Domain Adaptive Image Recognition
The Visual Domain Adaptation (VisDA) 2021 Challenge calls for unsupervis...
research
∙
08/18/2021
Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies
Humans are emotional creatures. Multiple modalities are often involved w...
research
∙
06/30/2021
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers....
research
∙
06/30/2021
Multi-Source Domain Adaptation for Object Detection
To reduce annotation labor associated with object detection, an increasi...
research
∙
03/19/2021
Computational Emotion Analysis From Images: Recent Advances and Future Directions
Emotions are usually evoked in humans by images. Recently, extensive res...
research
∙
11/25/2020
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
Thanks to large-scale labeled training data, deep neural networks (DNNs)...
research
∙
11/17/2020
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
Sentiment analysis of user-generated reviews or comments on products and...
research
∙
09/01/2020
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Large-scale labeled training datasets have enabled deep neural networks ...
research
∙
08/22/2020
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...
research
∙
06/23/2020
Rethinking Distributional Matching Based Domain Adaptation
Domain adaptation (DA) is a technique that transfers predictive models t...
research
∙
02/26/2020
Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey
In many practical applications, it is often difficult and expensive to o...
research
∙
02/12/2020
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 ...
research
∙
01/12/2020
Multi-source Domain Adaptation for Visual Sentiment Classification
Existing domain adaptation methods on visual sentiment classification ty...
research
∙
11/22/2019
Multi-source Distilling Domain Adaptation
Deep neural networks suffer from performance decay when there is domain ...
research
∙
10/27/2019
Multi-source Domain Adaptation for Semantic Segmentation
Simulation-to-real domain adaptation for semantic segmentation has been ...
research
∙
10/14/2019
Sketch-Specific Data Augmentation for Freehand Sketch Recognition
Sketch recognition remains a significant challenge due to the limited tr...
research
∙
10/03/2019
Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Survey
The wide popularity of digital photography and social networks has gener...
research
∙
09/11/2019
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
Existing methods on visual emotion analysis mainly focus on coarse-grain...
research
∙
09/02/2019
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...
research
∙
12/14/2018
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...
research
∙
11/05/2018
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...
research
∙
10/11/2018
A Novel Domain Adaptation Framework for Medical Image Segmentation
We propose a segmentation framework that uses deep neural networks and i...
research
∙
09/22/2018
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 ...
research
∙
03/24/2018
Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld
Large-scale labeled training datasets have enabled deep neural networks ...
research
∙
03/23/2018
SqueezeNext: Hardware-Aware Neural Network Design
One of the main barriers for deploying neural networks on embedded syste...
research
∙
11/22/2017