159 citations · 783 across the 25 of their papers we have counts for
20 papers · 1 filter
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion
James Z. Wang, Sicheng Zhao, Chenyan Wu +4
The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding o…
3rd Place Solution for VisDA 2021 Challenge -- Universally Domain Adaptive Image Recognition
Haojin Liao, Xiaolin Song, Sicheng Zhao +7
The Visual Domain Adaptation (VisDA) 2021 Challenge calls for unsupervised domain adaptation (UDA) methods that can deal with both input distribution shift and label set variance b…
Affective Image Content Analysis: Two Decades Review and New Perspectives
Sicheng Zhao, Xingxu Yao, Jufeng Yang +5
Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, ext…
Multi-Source Domain Adaptation for Object Detection
Xingxu Yao, Sicheng Zhao, Pengfei Xu +1
To reduce annotation labor associated with object detection, an increasing number of studies focus on transferring the learned knowledge from a labeled source domain to another unl…
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
Sicheng Zhao, Xuanbai Chen, Xiangyu Yue +7
Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of d…
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Sicheng Zhao, Xiangyu Yue, Shanghang Zhang +8
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively e…
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation
Sicheng Zhao, Yezhen Wang, Bo Li +5
Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data…
Emotion-Based End-to-End Matching Between Image and Music in Valence-Arousal Space
Sicheng Zhao, Yaxian Li, Xingxu Yao +4
Both images and music can convey rich semantics and are widely used to induce specific emotions. Matching images and music with similar emotions might help to make emotion percepti…
Rethinking Distributional Matching Based Domain Adaptation
Bo Li, Yezhen Wang, Tong Che +6
Domain adaptation (DA) is a technique that transfers predictive models trained on a labeled source domain to an unlabeled target domain, with the core difficulty of resolving distr…
MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation
Sicheng Zhao, Bo Li, Xiangyu Yue +2
Domain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or unlabeled target domain. Since the…
An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos
Sicheng Zhao, Yunsheng Ma, Yang Gu +6
Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i.e. extrac…
Multi-source Domain Adaptation for Visual Sentiment Classification
Chuang Lin, Sicheng Zhao, Lei Meng +1
Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain…
Multi-source Domain Adaptation for Semantic Segmentation
Sicheng Zhao, Bo Li, Xiangyu Yue +5
Simulation-to-real domain adaptation for semantic segmentation has been actively studied for various applications such as autonomous driving. Existing methods mainly focus on a sin…
Sketch-Specific Data Augmentation for Freehand Sketch Recognition
Ying Zheng, Hongxun Yao, Xiaoshuai Sun +3
Sketch recognition remains a significant challenge due to the limited training data and the substantial intra-class variance of freehand sketches for the same object. Conventional…
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
Sicheng Zhao, Zizhou Jia, Hui Chen +3
Existing methods on visual emotion analysis mainly focus on coarse-grained emotion classification, i.e. assigning an image with a dominant discrete emotion category. However, these…
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
Xiangyu Yue, Yang Zhang, Sicheng Zhao +3
We propose to harness the potential of simulation for the semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is tr…
A Novel Domain Adaptation Framework for Medical Image Segmentation
Amir Gholami, Shashank Subramanian, Varun Shenoy +6
We propose a segmentation framework that uses deep neural networks and introduce two innovations. First, we describe a biophysics-based domain adaptation method. Second, we propose…
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
Bichen Wu, Xuanyu Zhou, Sicheng Zhao +2
Earlier work demonstrates the promise of deep-learning-based approaches for point cloud segmentation; however, these approaches need to be improved to be practically useful. To thi…
Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld
Sicheng Zhao, Bichen Wu, Joseph Gonzalez +2
Large-scale labeled training datasets have enabled deep neural networks to excel on a wide range of benchmark vision tasks. However, in many applications it is prohibitively expens…
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Bichen Wu, Alvin Wan, Xiangyu Yue +6
Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadra…