60 citations · 110 across the 12 of their papers we have counts for
12 papers
Implicit 4D Gaussian Splatting for Fast Motion with Large Inter-Frame Displacements
Seung-gyeom Kim, Areum Kim, Yongjae Yoo +1
Recent 4D Gaussian Splatting (4DGS) methods often fail under fast motion with large inter-frame displacements, where Gaussian attributes are poorly learned during training, and fas…
Reference-Free Assessment of Physical Consistency in World Model-based Video Generation
Yun Oh, Sukmin Yun
We introduce reference-free measures for evaluating the physical consistency of generated videos, combining relative and absolute approaches to assess fidelity. Although tools like…
Improving Visual Representation Alignment Generation with GRPO
Shentong Mo, Sukmin Yun
Recent diffusion transformers have demonstrated strong image synthesis capabilities but remain inefficient to train due to weak alignment between generative and discriminative repr…
LVRPO: Language-Visual Alignment with GRPO for Multimodal Understanding and Generation
Shentong Mo, Sukmin Yun
Unified multimodal pretraining has emerged as a promising paradigm for jointly modeling language and vision within a single foundation model. However, existing approaches largely r…
Tabular Transfer Learning via Prompting LLMs
Jaehyun Nam, Woomin Song, Seong Hyeon Park +5
Learning with a limited number of labeled data is a central problem in real-world applications of machine learning, as it is often expensive to obtain annotations. To deal with the…
Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs
Woomin Song, Seunghyuk Oh, Sangwoo Mo +4
Large language models (LLMs) have shown remarkable performance in various natural language processing tasks. However, a primary constraint they face is the context limit, i.e., the…
IFSeg: Image-free Semantic Segmentation via Vision-Language Model
Sukmin Yun, Seong Hyeon Park, Paul Hongsuck Seo +1
Vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual…
Time Is MattEr: Temporal Self-supervision for Video Transformers
Sukmin Yun, Jaehyung Kim, Dongyoon Han +3
Understanding temporal dynamics of video is an essential aspect of learning better video representations. Recently, transformer-based architectural designs have been extensively ex…
Patch-level Representation Learning for Self-supervised Vision Transformers
Sukmin Yun, Hankook Lee, Jaehyung Kim +1
Recent self-supervised learning (SSL) methods have shown impressive results in learning visual representations from unlabeled images. This paper aims to improve their performance f…
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
Jongjin Park, Sukmin Yun, Jongheon Jeong +1
Semi-supervised learning (SSL) has been a powerful strategy to incorporate few labels in learning better representations. In this paper, we focus on a practical scenario that one a…
Regularizing Class-wise Predictions via Self-knowledge Distillation
Sukmin Yun, Jongjin Park, Kimin Lee +1
Deep neural networks with millions of parameters may suffer from poor generalization due to overfitting. To mitigate the issue, we propose a new regularization method that penalize…
Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee, Sukmin Yun, Kibok Lee +3
Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such…