613 citations · 1.6k across the 49 of their papers we have counts for
14 papers · 1 filter
Language-only Efficient Training of Zero-shot Composed Image Retrieval
Geonmo Gu, Sanghyuk Chun, Wonjae Kim +2
Composed image retrieval (CIR) task takes a composed query of image and text, aiming to search relative images for both conditions. Conventional CIR approaches need a training data…
Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired Images
Jiwon Kim, Byeongho Heo, Sangdoo Yun +2
Semantic correspondence methods have advanced to obtaining high-quality correspondences employing complicated networks, aiming to maximize the model capacity. However, despite the…
ProPILE: Probing Privacy Leakage in Large Language Models
Siwon Kim, Sangdoo Yun, Hwaran Lee +3
The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PI…
Masking meets Supervision: A Strong Learning Alliance
Byeongho Heo, Taekyung Kim, Sangdoo Yun +1
Pre-training with random masked inputs has emerged as a novel trend in self-supervised training. However, supervised learning still faces a challenge in adopting masking augmentati…
MPCHAT: Towards Multimodal Persona-Grounded Conversation
Jaewoo Ahn, Yeda Song, Sangdoo Yun +1
In order to build self-consistent personalized dialogue agents, previous research has mostly focused on textual persona that delivers personal facts or personalities. However, to f…
Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models
Geewook Kim, Hodong Lee, Daehee Kim +7
Recent advances in Large Language Models (LLMs) have stimulated a surge of research aimed at extending their applications to the visual domain. While these models exhibit promise i…
Who Wrote this Code? Watermarking for Code Generation
Taehyun Lee, Seokhee Hong, Jaewoo Ahn +5
Since the remarkable generation performance of large language models raised ethical and legal concerns, approaches to detect machine-generated text by embedding watermarks are bein…
What Do Self-Supervised Vision Transformers Learn?
Namuk Park, Wonjae Kim, Byeongho Heo +2
We present a comparative study on how and why contrastive learning (CL) and masked image modeling (MIM) differ in their representations and in their performance of downstream tasks…
Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild
Gyeongsik Moon, Hongsuk Choi, Sanghyuk Chun +2
Recovering 3D human mesh in the wild is greatly challenging as in-the-wild (ITW) datasets provide only 2D pose ground truths (GTs). Recently, 3D pseudo-GTs have been widely used to…
RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models
Seulki Park, Daeho Um, Hajung Yoon +3
With the extensive use of vision-language models in various downstream tasks, evaluating their robustness is crucial. In this paper, we propose a benchmark for assessing the robust…
Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproducts
Dongyoon Han, Junsuk Choe, Seonghyeok Chun +5
Supervised learning of image classifiers distills human knowledge into a parametric model through pairs of images and corresponding labels (X,Y). We argue that this simple and wide…
CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion
Geonmo Gu, Sanghyuk Chun, Wonjae Kim +3
This paper proposes a novel diffusion-based model, CompoDiff, for solving zero-shot Composed Image Retrieval (ZS-CIR) with latent diffusion. This paper also introduces a new synthe…
SeiT: Storage-Efficient Vision Training with Tokens Using 1% of Pixel Storage
Song Park, Sanghyuk Chun, Byeongho Heo +2
We need billion-scale images to achieve more generalizable and ground-breaking vision models, as well as massive dataset storage to ship the images (e.g., the LAION-4B dataset need…
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data
Jang-Hyun Kim, Sangdoo Yun, Hyun Oh Song
Diagnosing and cleaning data is a crucial step for building robust machine learning systems. However, identifying problems within large-scale datasets with real-world distributions…