Search Results for author:
Found 8 papers, 5 papers with code
Salient Object-Aware Background Generation using Text-Guided Diffusion Models
Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments.
HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach
Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on.
Staging E-Commerce Products for Online Advertising using Retrieval Assisted Image Generation
In addition, we show how our staging approach can enable animations of moving products leading to a video ad from a product image.
Revising deep learning methods in parking lot occupancy detection
Parking guidance systems have recently become a popular trend as a part of the smart cities' paradigm of development.
Ranked #1 on
Parking Space Occupancy
on PKLot
(F1-score metric)
VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search
Motivated by our observations in logged data on ad image search queries (given ad text), we formulate a keyword extraction problem, where a keyword extracted from the ad text (or its augmented version) serves as the ad image query.
Probabilistic Label Trees for Extreme Multi-label Classification
We first introduce the PLT model and discuss training and inference procedures and their computational costs.
On the computational complexity of the probabilistic label tree algorithms
First, we show that finding a tree with optimal training cost is NP-complete, nevertheless there are some tractable special cases with either perfect approximation or exact solution that can be obtained in linear time in terms of the number of labels $m$.
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels.
