Search Results for author:
Found 5 papers, 1 papers with code
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction
Given the huge amount of user opinions, it would be useful if we can automatically collect and aggregate the overall topical stance for a specific topic.
TailorGAN: Making User-Defined Fashion Designs
To overcome those limitations, we propose a novel self-supervised model to synthesize garment images with disentangled attributes (e. g., collar and sleeves) without paired data.
Netizen-Style Commenting on Fashion Photos: Dataset and Diversity Measures
To tackle this problem, we propose Netizen Style Commenting (NSC), to automatically generate characteristic comments to a user-contributed fashion photo.
Cultural Vocal Bursts Intensity Prediction
Image Captioning
+1
When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features
With the prevalence of e-commence websites and the ease of online shopping, consumers are embracing huge amounts of various options in products.
Who are the Devils Wearing Prada in New York City?
To answer this question, we design a novel system that consists of three major components: (1) constructing a large dataset from the New York Fashion Shows and New York street chic in order to understand the likely clothing fashion trends in New York, (2) utilizing a learning-based approach to discover fashion attributes as the representative characteristics of fashion trends, and (3) comparing the analysis results from the New York Fashion Shows and street-chic images to verify whether the fashion shows have actual influence on the people in New York City.
