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Is There a Better Source Distribution than Gaussian? Exploring Source Distributions for Image Flow Matching
Junho Lee, Kwanseok Kim, Joonseok Lee
Flow matching has emerged as a powerful generative modeling approach with flexible choices of source distribution. While Gaussian distributions are commonly used, the potential for…
Self-Guided Masked Autoencoder
Jeongwoo Shin, Inseo Lee, Junho Lee +1
Masked Autoencoder (MAE) is a self-supervised approach for representation learning, widely applicable to a variety of downstream tasks in computer vision. In spite of its success,…
Latent Diffusion Models with Masked AutoEncoders
Junho Lee, Jeongwoo Shin, Hyungwook Choi +1
In spite of the remarkable potential of Latent Diffusion Models (LDMs) in image generation, the desired properties and optimal design of the autoencoders have been underexplored. I…
Finding NeMo: Negative-mined Mosaic Augmentation for Referring Image Segmentation
Seongsu Ha, Chaeyun Kim, Donghwa Kim +3
Referring Image Segmentation is a comprehensive task to segment an object referred by a textual query from an image. In nature, the level of difficulty in this task is affected by…
Scalable Frame Sampling for Video Classification: A Semi-Optimal Policy Approach with Reduced Search Space
Junho Lee, Jeongwoo Shin, Seung Woo Ko +2
Given a video with frames, frame sampling is a task to select frames, so as to maximize the performance of a fixed video classifier. Not just brute-force search, but…