148 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Seoul National UniversityKR34 papers
- Sungkyunkwan UniversityKR17 papers
- Yonsei UniversityKR16 papers
- Korea UniversityKR15 papers
- Korea Institute for Advanced StudyKR14 papers
- University of PittsburghUS14 papers
- High Energy Accelerator Research OrganizationJP11 papers
- Kyungpook National UniversityKR11 papers
- Institute for Basic ScienceKR10 papers
- New Generation University CollegeET10 papers
- Tohoku UniversityJP10 papers
- Chonnam National UniversityKR9 papers
22 papers · 1 filter
Unbalanced Optimal Transport for Efficient Visual Document Retrieval
Hoyeon Shin, Jeongyeon Kim, Yeong Jun Koh +2
Vision-Language Models(VLMs)have advanced Visual Document Retrieval (VDR) by enabling OCR-free, multi-vector representations. However, these models generate excessive tokens to pre…
Adjudicated Captioning: Multi-Agent Alignment Scoring and Consensus-Distilled Beam Arbitration for Strict Zero-Shot Image Captioning
Duy Tran Thanh, Thien-Phuc Doan, Long Nguyen-Vu +1
Zero-shot image captioning (ZIC) describes images without paired image-caption supervision during captioner training, relying on text-only corpora and frozen pretrained image-text…
Edges Before Embeddings: A Confidence-Aware Blur Gate for Vision-Language Pipelines
Duy Tran Thanh
Production vision pipelines silently degrade on blurry input, wasting compute on downstream OCR, retrieval, and vision-language model (VLM) calls that cannot recover a usable outpu…
Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation
Eun Som Jeon, Jisoo Lee, Huisu Lim +3
Wearable sensor-based human gait analysis holds great promise in healthcare, rehabilitation, clinical diagnosis and monitoring, and sports activities. Specifically, ground reaction…
Generalized Zero-Shot Learning for Point Cloud Segmentation with Evidence-Based Dynamic Calibration
Hyeonseok Kim, Byeongkeun Kang, Yeejin Lee
Generalized zero-shot semantic segmentation of 3D point clouds aims to classify each point into both seen and unseen classes. A significant challenge with these models is their ten…
Intra-class Patch Swap for Self-Distillation
Hongjun Choi, Eun Som Jeon, Ankita Shukla +1
Knowledge distillation (KD) is a valuable technique for compressing large deep learning models into smaller, edge-suitable networks. However, conventional KD frameworks rely on pre…
Lightweight Deepfake Detection Based on Multi-Feature Fusion
Siddiqui Muhammad Yasir, Hyun Kim
Deepfake technology utilizes deep learning based face manipulation techniques to seamlessly replace faces in videos creating highly realistic but artificially generated content. Al…
Content-Aware Preserving Image Generation
Giang H. Le, Anh Q. Nguyen, Byeongkeun Kang +1
Remarkable progress has been achieved in image generation with the introduction of generative models. However, precisely controlling the content in generated images remains a chall…
MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation
Duc Dang Trung Tran, Byeongkeun Kang, Yeejin Lee
Recently, transformer-based techniques incorporating superpoints have become prevalent in 3D instance segmentation. However, they often encounter an over-segmentation problem, espe…
Batch Transformer: Look for Attention in Batch
Myung Beom Her, Jisu Jeong, Hojoon Song +1
Facial expression recognition (FER) has received considerable attention in computer vision, with "in-the-wild" environments such as human-computer interaction. However, FER images…
StochCA: A Novel Approach for Exploiting Pretrained Models with Cross-Attention
Seungwon Seo, Suho Lee, Sangheum Hwang
Utilizing large-scale pretrained models is a well-known strategy to enhance performance on various target tasks. It is typically achieved through fine-tuning pretrained models on t…
Pixel-Level Clustering Network for Unsupervised Image Segmentation
Cuong Manh Hoang, Byeongkeun Kang
While image segmentation is crucial in various computer vision applications, such as autonomous driving, grasping, and robot navigation, annotating all objects at the pixel-level f…
FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object Localization
Sejin Park, Taehyung Lee, Yeejin Lee +1
This work addresses the task of class-incremental weakly supervised object localization (CI-WSOL). The goal is to incrementally learn object localization for novel classes using on…
Sampling Agnostic Feature Representation for Long-Term Person Re-identification
Seongyeop Yang, Byeongkeun Kang, Yeejin Lee
Person re-identification is a problem of identifying individuals across non-overlapping cameras. Although remarkable progress has been made in the re-identification problem, it is…
Dense Depth Estimation from Multiple 360-degree Images Using Virtual Depth
Seongyeop Yang, Kunhee Kim, Yeejin Lee
In this paper, we propose a dense depth estimation pipeline for multiview 360° images. The proposed pipeline leverages a spherical camera model that compensates for radial distorti…
A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks
Jihyo Kim, Jiin Koo, Sangheum Hwang
Deep neural networks have achieved outstanding performance over various tasks, but they have a critical issue: over-confident predictions even for completely unknown samples. Many…
Layer-specific Optimization for Mixed Data Flow with Mixed Precision in FPGA Design for CNN-based Object Detectors
Duy Thanh Nguyen, Hyun Kim, Hyuk-Jae Lee
Convolutional neural networks (CNNs) require both intensive computation and frequent memory access, which lead to a low processing speed and large power dissipation. Although the c…
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
Jungbeom Lee, Eunji Kim, Sungmin Lee +2
The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations. Most methods based on…
Multimodal Dual Attention Memory for Video Story Question Answering
Kyung-Min Kim, Seong-Ho Choi, Jin-Hwa Kim +1
We propose a video story question-answering (QA) architecture, Multimodal Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism with late fusion. MDAM use…
Quantitative Phase Imaging and Artificial Intelligence: A Review
YoungJu Jo, Hyungjoo Cho, Sang Yun Lee +4
Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have opened up the possibility of an exciting frontier. The fast and label-free nature of QPI e…
Vehicle Image Generation Going Well with The Surroundings
Jeesoo Kim, Jangho Kim, Jaeyoung Yoo +2
Since the generative neural networks have made a breakthrough in the image generation problem, lots of researches on their applications have been studied such as image restoration,…
Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations
Yoonho Boo, Wonyong Sung
Deep neural networks (DNNs) usually demand a large amount of operations for real-time inference. Especially, fully-connected layers contain a large number of weights, thus they usu…