26 citations · 36 across the 2 of their papers we have counts for
6 papers
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis
Zhongkai Sun, Prathusha Sarma, William Sethares +1
Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform no…
Shallow Domain Adaptive Embeddings for Sentiment Analysis
Prathusha K Sarma, Yingyu Liang, William A Sethares
This paper proposes a way to improve the performance of existing algorithms for text classification in domains with strong language semantics. We propose a domain adaptation layer…
Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis
Zhongkai Sun, Prathusha K Sarma, William Sethares +1
This paper learns multi-modal embeddings from text, audio, and video views/modes of data in order to improve upon down-stream sentiment classification. The experimental framework a…
SpecNet: Spectral Domain Convolutional Neural Network
Bochen Guan, Jinnian Zhang, William A. Sethares +2
The memory consumption of most Convolutional Neural Network (CNN) architectures grows rapidly with increasing depth of the network, which is a major constraint for efficient networ…
Video Logo Retrieval based on local Features
Bochen Guan, Hanrong Ye, Hong Liu +1
Estimation of the frequency and duration of logos in videos is important and challenging in the advertisement industry as a way of estimating the impact of ad purchases. Since logo…
Domain Adapted Word Embeddings for Improved Sentiment Classification
Prathusha K Sarma, YIngyu Liang, William A Sethares
Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest. This paper proposes a m…