I. Overview
“Visual perception, a primary source of information for a human, is the ability to interpret the surrounding environment through the visual system.”
Characteristics of human intelligence
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Hierarchical & sparse neurons
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Multisensory & multimodal data processsing
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Independent, but flexible knowledge interaction
We focus on building human-like visual systems by collaboratively and interactively leveraging semantic and geometric knowledge, and understanding fundamental machine learning techniques such as self-supervised learning, multimodal learning, one-shot learning, domain adaptation, meta learning, active learning, etc.
II. Semantic perception
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Object detection and recognition
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Semantic segmentation, instance segmentation, video segmentation
>> Comparison of semantic segmentation, classification and localization, object detection and instance segmentation (Li, Johnson and Yeung, 2017)
Related publications
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S. Lee, J. Kim, J.S. Yoon, S. Shin, O. Bailo, N. Kim, T.H. Lee, H.S. Hong, S.H. Han, and I.S. Kweon, ”VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition", IEEE International Conference on Computer Vision (ICCV) 2017. Detection Dataset Multi-task learning
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J. Kim, S. Lee, T.H. Oh, and I.S. Kweon, “Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition”, AAAI Conference on Artificial Intelligence (AAAI) 2018. Classification Few-shot learning
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J. Kim, T.H. Oh, S. Lee, F. Pan, and I.S. Kweon, “Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019. Classification One-shot learning
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F. Pan, I. Shin, F. Rameau, S. Lee, and I.S. Kweon, “Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020. Semantic segmentation Domain adaptation
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J.S. Yoon, F. Rameau, J. Kim, S. Lee, S. Shin, and I.S. Kweon, “Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks”, IEEE International Conference on Computer Vision (ICCV) 2017. Video object segmentation
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O. Bailo, S. Lee, F. Rameau, J.S. Yoon, and I.S. Kweon, “Robust Road Marking Detection and Recognition Using Density-Based Grouping and Machine Learning Techniques”, IEEE Winter Conference on Applications of Computer Vision (WACV) 2017. Detection
III. 3D visual perception
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Monocular depth and motion estimation
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Correspondence problem: stereo matching, optical flow
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3D reconstrcution, visual odometry, simultaneous localization and mapping (SLAM)
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Neural radiance fields (NeRF)
Related publications
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S. Lee, F. Rameau, F. Pan, and I.S. Kweon, ”Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation", IEEE International Conference on Computer Vision (ICCV) 2021. monocular depth self-supervised learning
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S. Lee, S. Im, S. Lin, and I.S. Kweon, “Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency”, AAAI Conference on Artificial Intelligence (AAAI) 2021. monocular depth self-supervised learning
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U. Shin, K. Lee, S. Lee, and I.S. Kweon, “Self-supervised Depth and Ego-motion Estimation for Monocular Thermal Video Using Multi-spectral Consistency Loss”, IEEE Robotics and Automation Letters (RA-L) 2022. monocular depth sensor fusion self-supervised learning
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S. Lee, S. Im, S. Lin, and I.S. Kweon, “Learning Residual Flow as Dynamic Motion from Stereo Videos”, IEEE International Conference on Intelligent Robots and Systems (IROS) 2019. optical flow self-supervised learning
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A. Bangunharcana, J.W. Cho, S. Lee, I.S. Kweon, K.S. Kim, and S. Kim, ”Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation”, IEEE International Conference on Intelligent Robots and Systems (IROS) 2021. stereo matching