Publications (11)
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment
Yin Zhao, Minquan Wang, Longjun Cai
Eliminating the covariate shift cross domains is one of the common methods to deal with the issue of domain shift in visual unsupervised domain adaptation. However, current alignme…
Predicting the Popularity of Online Videos via Deep Neural Networks
Yue Mao, Yi Shen, Gang Qin +1
Predicting the popularity of online videos is important for video streaming content providers. This is a challenging problem because of the following two reasons. First, the proble…
Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark
Xun Gao, Yin Zhao, Jie Zhang +1
Recognizing the emotional state of people is a basic but challenging task in video understanding. In this paper, we propose a new task in this field, named Pairwise Emotional Relat…
Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context
Jie Zhang, Yin Zhao, Longjun Cai +2
Predicting the emotional impact of videos using machine learning is a challenging task considering the varieties of modalities, the complicated temporal contex of the video as well…
Constrained Sequence-to-Tree Generation for Hierarchical Text Classification
Chao Yu, Yi Shen, Yue Mao +1
Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy. The majority of pr…
Hybrid Curriculum Learning for Emotion Recognition in Conversation
Lin Yang, Yi Shen, Yue Mao +1
Emotion recognition in conversation (ERC) aims to detect the emotion label for each utterance. Motivated by recent studies which have proven that feeding training examples in a mea…