20 citations · 32 across the 18 of their papers we have counts for
18 papers
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation
Seokil Ham, Hee-Seon Kim, Sangmin Woo +1
Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain large…
Flow-Assisted Motion Learning Network for Weakly-Supervised Group Activity Recognition
Muhammad Adi Nugroho, Sangmin Woo, Sumin Lee +4
Weakly-Supervised Group Activity Recognition (WSGAR) aims to understand the activity performed together by a group of individuals with the video-level label and without actor-level…
Diffusion Model Patching via Mixture-of-Prompts
Seokil Ham, Sangmin Woo, Jin-Young Kim +3
We present Diffusion Model Patching (DMP), a simple method to boost the performance of pre-trained diffusion models that have already reached convergence, with a negligible increas…
RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models
Sangmin Woo, Jaehyuk Jang, Donguk Kim +2
Recent advancements in Large Vision Language Models (LVLMs) have revolutionized how machines understand and generate textual responses based on visual inputs, yet they often produc…
Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Sangmin Woo, Donguk Kim, Jaehyuk Jang +2
Large Vision Language Models (LVLMs) demonstrate strong capabilities in visual understanding and description, yet often suffer from hallucinations, attributing incorrect or mislead…
Spatio-Temporal Proximity-Aware Dual-Path Model for Panoramic Activity Recognition
Sumin Lee, Yooseung Wang, Sangmin Woo +1
Panoramic Activity Recognition (PAR) seeks to identify diverse human activities across different scales, from individual actions to social group and global activities in crowded pa…
Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
Byeongjun Park, Hyojun Go, Jin-Young Kim +3
Diffusion models have achieved remarkable success across a range of generative tasks. Recent efforts to enhance diffusion model architectures have reimagined them as a form of mult…
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D
Sangmin Woo, Byeongjun Park, Hyojun Go +2
Recent progress in single-image 3D generation highlights the importance of multi-view coherency, leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale…
Modality Mixer Exploiting Complementary Information for Multi-modal Action Recognition
Sumin Lee, Sangmin Woo, Muhammad Adi Nugroho +1
Due to the distinctive characteristics of sensors, each modality exhibits unique physical properties. For this reason, in the context of multi-modal action recognition, it is impor…
Denoising Task Routing for Diffusion Models
Byeongjun Park, Sangmin Woo, Hyojun Go +2
Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent…
Audio-Visual Glance Network for Efficient Video Recognition
Muhammad Adi Nugroho, Sangmin Woo, Sumin Lee +1
Deep learning has made significant strides in video understanding tasks, but the computation required to classify lengthy and massive videos using clip-level video classifiers rema…
Sketch-based Video Object Localization
Sangmin Woo, So-Yeong Jeon, Jinyoung Park +3
We introduce Sketch-based Video Object Localization (SVOL), a new task aimed at localizing spatio-temporal object boxes in video queried by the input sketch. We first outline the c…
Towards Good Practices for Missing Modality Robust Action Recognition
Sangmin Woo, Sumin Lee, Yeonju Park +2
Standard multi-modal models assume the use of the same modalities in training and inference stages. However, in practice, the environment in which multi-modal models operate may no…
Temporal Flow Mask Attention for Open-Set Long-Tailed Recognition of Wild Animals in Camera-Trap Images
Jeongsoo Kim, Sangmin Woo, Byeongjun Park +1
Camera traps, unmanned observation devices, and deep learning-based image recognition systems have greatly reduced human effort in collecting and analyzing wildlife images. However…
Modality Mixer for Multi-modal Action Recognition
Sumin Lee, Sangmin Woo, Yeonju Park +2
In multi-modal action recognition, it is important to consider not only the complementary nature of different modalities but also global action content. In this paper, we propose a…
Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos
Sangmin Woo, Jinyoung Park, Inyong Koo +3
Natural Language Video Grounding (NLVG) aims to localize time segments in an untrimmed video according to sentence queries. In this work, we present a new paradigm named Explore-An…
What and When to Look?: Temporal Span Proposal Network for Video Relation Detection
Sangmin Woo, Junhyug Noh, Kangil Kim
Identifying relations between objects is central to understanding the scene. While several works have been proposed for relation modeling in the image domain, there have been many…
Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions
Sangmin Woo, Junhyug Noh, Kangil Kim
In this work, we seek new insights into the underlying challenges of the Scene Graph Generation (SGG) task. Quantitative and qualitative analysis of the Visual Genome dataset impli…