41 citations · 93 across the 15 of their papers we have counts for
17 papers
AAPL: Adding Attributes to Prompt Learning for Vision-Language Models
Gahyeon Kim, Sohee Kim, Seokju Lee
Recent advances in large pre-trained vision-language models have demonstrated remarkable performance on zero-shot downstream tasks. Building upon this, recent studies, such as CoOp…
CLIP Can Understand Depth
Sohee Kim, Jisu Kang, Dunam Kim +1
In this paper, we demonstrate that CLIP can also be adapted to downstream tasks where its vision-language alignment is suboptimally learned during pre-training on web-crawled data,…
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic Segmentation
Fei Pan, Xu Yin, Seokju Lee +3
Unsupervised domain adaptation (UDA) has been a potent technique to handle the lack of annotations in the target domain, particularly in semantic segmentation task. This study intr…
ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Fei Pan, Sungsu Hur, Seokju Lee +2
Open compound domain adaptation (OCDA) considers the target domain as the compound of multiple unknown homogeneous subdomains. The goal of OCDA is to minimize the domain gap betwee…
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation
Seokju Lee, Francois Rameau, Fei Pan +1
Estimating the motion of the camera together with the 3D structure of the scene from a monocular vision system is a complex task that often relies on the so-called scene rigidity a…
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation
Antyanta Bangunharcana, Jae Won Cho, Seokju Lee +3
Volumetric deep learning approach towards stereo matching aggregates a cost volume computed from input left and right images using 3D convolutions. Recent works showed that utiliza…
Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss
Ukcheol Shin, Kyunghyun Lee, Seokju Lee +1
A thermal camera can robustly capture thermal radiation images under harsh light conditions such as night scenes, tunnels, and disaster scenarios. However, despite this advantage,…
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency
Seokju Lee, Sunghoon Im, Stephen Lin +1
We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervis…
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
Chaoning Zhang, Philipp Benz, Dawit Mureja Argaw +5
ResNet or DenseNet? Nowadays, most deep learning based approaches are implemented with seminal backbone networks, among them the two arguably most famous ones are ResNet and DenseN…
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Fei Pan, Inkyu Shin, Francois Rameau +2
Convolutional neural network-based approaches have achieved remarkable progress in semantic segmentation. However, these approaches heavily rely on annotated data which are labor i…
Instance-wise Depth and Motion Learning from Monocular Videos
Seokju Lee, Sunghoon Im, Stephen Lin +1
We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervis…
Learning Residual Flow as Dynamic Motion from Stereo Videos
Seokju Lee, Sunghoon Im, Stephen Lin +1
We present a method for decomposing the 3D scene flow observed from a moving stereo rig into stationary scene elements and dynamic object motion. Our unsupervised learning framewor…
Visuomotor Understanding for Representation Learning of Driving Scenes
Seokju Lee, Junsik Kim, Tae-Hyun Oh +4
Dashboard cameras capture a tremendous amount of driving scene video each day. These videos are purposefully coupled with vehicle sensing data, such as from the speedometer and ine…
Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
Junsik Kim, Tae-Hyun Oh, Seokju Lee +2
In daily life, graphic symbols, such as traffic signs and brand logos, are ubiquitously utilized around us due to its intuitive expression beyond language boundary. We tackle an op…
Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition
Junsik Kim, Seokju Lee, Tae-Hyun Oh +1
Recent advances in visual recognition show overarching success by virtue of large amounts of supervised data. However,the acquisition of a large supervised dataset is often challen…
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
Seokju Lee, Junsik Kim, Jae Shin Yoon +7
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point…
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
Jae Shin Yoon, Francois Rameau, Junsik Kim +3
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from…