86 citations · 685 across the 67 of their papers we have counts for
45 papers · 1 filter
MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse Inputs
Seunghyeon Seo, Donghoon Han, Yeonjin Chang +1
Neural Radiance Field (NeRF) has broken new ground in the novel view synthesis due to its simple concept and state-of-the-art quality. However, it suffers from severe performance d…
Semantics-Guided Object Removal for Facial Images: with Broad Applicability and Robust Style Preservation
Jookyung Song, Yeonjin Chang, Seonguk Park +1
Object removal and image inpainting in facial images is a task in which objects that occlude a facial image are specifically targeted, removed, and replaced by a properly reconstru…
Imposing Consistency for Optical Flow Estimation
Jisoo Jeong, Jamie Menjay Lin, Fatih Porikli +1
Imposing consistency through proxy tasks has been shown to enhance data-driven learning and enable self-supervision in various tasks. This paper introduces novel and effective cons…
MatteFormer: Transformer-Based Image Matting via Prior-Tokens
GyuTae Park, SungJoon Son, JaeYoung Yoo +2
In this paper, we propose a transformer-based image matting model called MatteFormer, which takes full advantage of trimap information in the transformer block. Our method first in…
Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation
John Yang, Yash Bhalgat, Simyung Chang +2
While hand pose estimation is a critical component of most interactive extended reality and gesture recognition systems, contemporary approaches are not optimized for computational…
MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
JongMok Kim, Jooyoung Jang, Seunghyeon Seo +3
Many recent semi-supervised learning (SSL) studies build teacher-student architecture and train the student network by the generated supervisory signal from the teacher. Data augme…
Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation for Semantic Segmentation
Inseop Chung, Jayeon Yoo, Nojun Kwak
"Self-training" has become a dominant method for semantic segmentation via unsupervised domain adaptation (UDA). It creates a set of pseudo labels for the target domain to give exp…
Few-Shot Object Detection by Attending to Per-Sample-Prototype
Hojun Lee, Myunggi Lee, Nojun Kwak
Few-shot object detection aims to detect instances of specific categories in a query image with only a handful of support samples. Although this takes less effort than obtaining en…
Normalization Matters in Weakly Supervised Object Localization
Jeesoo Kim, Junsuk Choe, Sangdoo Yun +1
Weakly-supervised object localization (WSOL) enables finding an object using a dataset without any localization information. By simply training a classification model using only im…
Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation
Inseop Chung, Daesik Kim, Nojun Kwak
We propose a novel method that tackles the problem of unsupervised domain adaptation for semantic segmentation by maximizing the cosine similarity between the source and the target…
The U-Net based GLOW for Optical-Flow-free Video Interframe Generation
Saem Park, Donghoon Han, Nojun Kwak
Video frame interpolation is the task of creating an interframe between two adjacent frames along the time axis. So, instead of simply averaging two adjacent frames to create an in…
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation
Hyojin Park, Jayeon Yoo, Seohyeong Jeong +2
Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the curre…
TTVOS: Lightweight Video Object Segmentation with Adaptive Template Attention Module and Temporal Consistency Loss
Hyojin Park, Ganesh Venkatesh, Nojun Kwak
Semi-supervised video object segmentation (semi-VOS) is widely used in many applications. This task is tracking class-agnostic objects from a given target mask. For doing this, var…
Procrustean Regression Networks: Learning 3D Structure of Non-Rigid Objects from 2D Annotations
Sungheon Park, Minsik Lee, Nojun Kwak
We propose a novel framework for training neural networks which is capable of learning 3D information of non-rigid objects when only 2D annotations are available as ground truths.…
SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation
John Yang, Hyung Jin Chang, Seungeui Lee +1
3D hand pose estimation based on RGB images has been studied for a long time. Most of the studies, however, have performed frame-by-frame estimation based on independent static ima…
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud
Jaeseok Choi, Yeji Song, Nojun Kwak
Data augmentation has greatly contributed to improving the performance in image recognition tasks, and a lot of related studies have been conducted. However, data augmentation on 3…
Interpolation-based semi-supervised learning for object detection
Jisoo Jeong, Vikas Verma, Minsung Hyun +2
Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection…
KL-Divergence-Based Region Proposal Network for Object Detection
Geonseok Seo, Jaeyoung Yoo, Jaeseok Choi +1
The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task. Howe…
Position-based Scaled Gradient for Model Quantization and Pruning
Jangho Kim, KiYoon Yoo, Nojun Kwak
We propose the position-based scaled gradient (PSG) that scales the gradient depending on the position of a weight vector to make it more compression-friendly. First, we theoretica…
LSQ+: Improving low-bit quantization through learnable offsets and better initialization
Yash Bhalgat, Jinwon Lee, Markus Nagel +2
Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently employed in popular efficient architectures can also result in negative activation values, w…
QKD: Quantization-aware Knowledge Distillation
Jangho Kim, Yash Bhalgat, Jinwon Lee +2
Quantization and Knowledge distillation (KD) methods are widely used to reduce memory and power consumption of deep neural networks (DNNs), especially for resource-constrained edge…
Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction
Daesik Kim, Gyujeong Lee, Jisoo Jeong +1
In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable kno…
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image
Jaeyoung Yoo, Hojun Lee, Inseop Chung +2
In multi-object detection using neural networks, the fundamental problem is, "How should the network learn a variable number of bounding boxes in different input images?". Previous…
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo +3
Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset…