34 citations · 56 across the 67 of their papers we have counts for
12 papers · 1 filter
EPIC: Efficient Predicate-Guided Inference-Time Control for Compositional Text-to-Image Generation
Sunung Mun, Sunghyun Cho, Jungseul Ok
Recent text-to-image (T2I) generators can synthesize realistic images, but still struggle with compositional prompts involving multiple objects, counts, attributes, and relations.…
POS-ISP: Pipeline Optimization at the Sequence Level for Task-aware ISP
Jiyun Won, Heemin Yang, Woohyeok Kim +2
Recent work has explored optimizing image signal processing (ISP) pipelines for various tasks by composing predefined modules and adapting them to task-specific objectives. However…
ChimeraLoRA: Multi-Head LoRA-Guided Synthetic Datasets
Hoyoung Kim, Minwoo Jang, Jabin Koo +2
Beyond general recognition tasks, specialized domains and fine-grained settings often encounter data scarcity, especially for tail classes. To obtain less biased and more reliable…
Visual Prompt Discovery via Semantic Exploration
Jaechang Kim, Yotaro Shimose, Zhao Wang +3
LVLMs encounter significant challenges in image understanding and visual reasoning, leading to critical perception failures. Visual prompts, which incorporate image manipulation co…
Edge-Aware Image Manipulation via Diffusion Models with a Novel Structure-Preservation Loss
Minsu Gong, Nuri Ryu, Jungseul Ok +1
Recent advances in image editing leverage latent diffusion models (LDMs) for versatile, text-prompt-driven edits across diverse tasks. Yet, maintaining pixel-level edge structures-…
Addressing Text Embedding Leakage in Diffusion-based Image Editing
Sunung Mun, Jinhwan Nam, Sunghyun Cho +1
Text-based image editing, powered by generative diffusion models, lets users modify images through natural-language prompts and has dramatically simplified traditional workflows. D…
Active Prompt Learning with Vision-Language Model Priors
Hoyoung Kim, Seokhee Jin, Changhwan Sung +2
Vision-language models (VLMs) have demonstrated remarkable zero-shot performance across various classification tasks. Nonetheless, their reliance on hand-crafted text prompts for e…
CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment
Hyeongmin Lee, Kyoungkook Kang, Jungseul Ok +1
Recent image tone adjustment (or enhancement) approaches have predominantly adopted supervised learning for learning human-centric perceptual assessment. However, these approaches…
Active Label Correction for Semantic Segmentation with Foundation Models
Hoyoung Kim, Sehyun Hwang, Suha Kwak +1
Training and validating models for semantic segmentation require datasets with pixel-wise annotations, which are notoriously labor-intensive. Although useful priors such as foundat…
Active Learning for Semantic Segmentation with Multi-class Label Query
Sehyun Hwang, Sohyun Lee, Hoyoung Kim +3
This paper proposes a new active learning method for semantic segmentation. The core of our method lies in a new annotation query design. It samples informative local image regions…
Adaptive Superpixel for Active Learning in Semantic Segmentation
Hoyoung Kim, Minhyeon Oh, Sehyun Hwang +2
Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learn…
Towards Sequence-Level Training for Visual Tracking
Minji Kim, Seungkwan Lee, Jungseul Ok +2
Despite the extensive adoption of machine learning on the task of visual object tracking, recent learning-based approaches have largely overlooked the fact that visual tracking is…