177 citations · 387 across the 7 of their papers we have counts for
9 papers
Cycle Consistency as Reward: Learning Image-Text Alignment without Human Preferences
Hyojin Bahng, Caroline Chan, Fredo Durand +1
Measuring alignment between language and vision is a fundamental challenge, especially as multimodal data becomes increasingly detailed and complex. Existing methods often rely on…
Scalable Optimization in the Modular Norm
Tim Large, Yang Liu, Minyoung Huh +3
To improve performance in contemporary deep learning, one is interested in scaling up the neural network in terms of both the number and the size of the layers. When ramping up the…
Exploring Visual Prompts for Adapting Large-Scale Models
Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan +1
We investigate the efficacy of visual prompting to adapt large-scale models in vision. Following the recent approach from prompt tuning and adversarial reprogramming, we learn a si…
Exploring Unlabeled Faces for Novel Attribute Discovery
Hyojin Bahng, Sunghyo Chung, Seungjoo Yoo +1
Despite remarkable success in unpaired image-to-image translation, existing systems still require a large amount of labeled images. This is a bottleneck for their real-world applic…
ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed
Cheonbok Park, Chunggi Lee, Hyojin Bahng +5
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamic…
Learning De-biased Representations with Biased Representations
Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun +2
Many machine learning algorithms are trained and evaluated by splitting data from a single source into training and test sets. While such focus on in-distribution learning scenario…
Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks
Seungjoo Yoo, Hyojin Bahng, Sunghyo Chung +3
Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount o…
MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation
David Keetae Park, Seungjoo Yoo, Hyojin Bahng +2
Recently, generative adversarial networks (GANs) have shown promising performance in generating realistic images. However, they often struggle in learning complex underlying modali…
Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation
Hyojin Bahng, Seungjoo Yoo, Wonwoong Cho +4
This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generat…