26 citations · 62 across the 11 of their papers we have counts for
12 papers
Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention
Liang Shang, William A. Sethares, Anusha Adluru +4
Precise characterization of stroke lesions from MRI data has immense value in prognosticating clinical and cognitive outcomes following a stroke. Manual stroke lesion segmentation…
Segmenting Small Stroke Lesions with Novel Labeling Strategies
Liang Shang, Zhengyang Lou, Andrew L. Alexander +4
Deep neural networks have demonstrated exceptional efficacy in stroke lesion segmentation. However, the delineation of small lesions, critical for stroke diagnosis, remains a chall…
Learning Label Hierarchy with Supervised Contrastive Learning
Ruixue Lian, William A. Sethares, Junjie Hu
Supervised contrastive learning (SCL) frameworks treat each class as independent and thus consider all classes to be equally important. This neglects the common scenario in which l…
Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention
Liang Shang, Yanli Liu, Zhengyang Lou +4
Convolutional neural networks (CNNs) and vision transformers (ViTs) have achieved remarkable success in various vision tasks. However, many architectures do not consider interactio…
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…
Model-Driven Applications of Fractional Derivatives and Integrals
William A. Sethares, Selçuk Ş. Bayın
Fractional order derivatives and integrals (differintegrals) are viewed from a frequency-domain perspective using the formalism of Riesz, providing a computational tool as well as…
Topology of Musical Data
Ryan Budney, William Sethares
The musical realm is a promising area in which to expect to find nontrivial topological structures. This paper describes several kinds of metrics on musical data, and explores the…