Search Results for author:
Found 7 papers, 3 papers with code
Learning Label Hierarchy with Supervised Contrastive Learning
This paper introduces a family of Label-Aware SCL methods (LASCL) that incorporates hierarchical information to SCL by leveraging similarities between classes, resulting in creating a more well-structured and discriminative feature space.
Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention
Convolutional neural networks (CNNs) and vision transformers (ViTs) have achieved remarkable success in various vision tasks.
Shallow Domain Adaptive Embeddings for Sentiment Analysis
This paper proposes a way to improve the performance of existing algorithms for text classification in domains with strong language semantics.
SpecNet: Spectral Domain Convolutional Neural Network
Often, these feature maps mimic natural photographs in the sense that their energy is concentrated in the spectral domain.
Video Logo Retrieval based on local Features
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.
Domain Adapted Word Embeddings for Improved Sentiment Classification
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.
Model-Driven Applications of Fractional Derivatives and Integrals
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 a way to interpret the operations in the frequency domain.
