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Researcher profile

Kuk-Jin Yoon

Kuk-Jin Yoon contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioneess.IVRoboticsArtificial IntelligenceGraphicsHuman-Computer Interaction

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Published work

14 published item(s)

preprint2026arXiv

HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models

End-to-end autonomous driving has emerged as a compelling alternative to traditional modular pipelines by directly mapping raw sensor data to driving actions. While recent approaches achieve strong performance on single-domain datasets, their performance degrades significantly when trained jointly across multiple heterogeneous domains. In practice, however, autonomous systems must operate across diverse environments with heterogeneous distributions, including different cities, sensor configurations, and traffic patterns, without domain-specific retraining. This gap highlights a key challenge in multi-domain learning: domain-specific variations across heterogeneous domains introduce conflicting learning signals, driving models toward compromised solutions that are suboptimal across domains. To address this, we propose a trajectory-driven learning paradigm that organizes training around planning trajectories, enabling the model to capture domain-invariant representations of driving intent. Furthermore, we incorporate a world model that predicts future latent features conditioned on ego actions, improving feature consistency and mitigating domain-induced biases. We evaluate our approach on three benchmarks, nuScenes, NAVSIM, and the Waymo end-to-end dataset, and show substantial improvements over existing methods across all domains. Our results demonstrate that a single unified model can be trained on heterogeneous datasets while maintaining strong performance within each domain, highlighting a step toward scalable real-world deployment. We will make our code publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Task-oriented Learnable Diffusion Timesteps for Universal Few-shot Learning of Dense Tasks

Denoising diffusion probabilistic models have brought tremendous advances in generative tasks, achieving state-of-the-art performance thus far. Current diffusion model-based applications exploit the power of learned visual representations from multistep forward-backward Markovian processes for single-task prediction tasks by attaching a task-specific decoder. However, the heuristic selection of diffusion timestep features still heavily relies on empirical intuition, often leading to sub-optimal performance biased towards certain tasks. To alleviate this constraint, we investigate the significance of versatile diffusion timestep features by adaptively selecting timesteps best suited for the few-shot dense prediction task, evaluated on an arbitrary unseen task. To this end, we propose two modules: Task-aware Timestep Selection (TTS) to select ideal diffusion timesteps based on timestep-wise losses and similarity scores, and Timestep Feature Consolidation (TFC) to consolidate the selected timestep features to improve the dense predictive performance in a few-shot setting. Accompanied by our parameter-efficient fine-tuning adapter, our framework effectively achieves superiority in dense prediction performance given only a few support queries. We empirically validate our learnable timestep consolidation method on the large-scale challenging Taskonomy dataset for dense prediction, particularly for practical universal and few-shot learning scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DGSS : Domain Generalized Semantic Segmentation using Iterative Style Mining and Latent Representation Alignment

Semantic segmentation algorithms require access to well-annotated datasets captured under diverse illumination conditions to ensure consistent performance. However, poor visibility conditions at varying illumination conditions result in laborious and error-prone labeling. Alternatively, using synthetic samples to train segmentation algorithms has gained interest with the drawback of domain gap that results in sub-optimal performance. While current state-of-the-art (SoTA) have proposed different mechanisms to bridge the domain gap, they still perform poorly in low illumination conditions with an average performance drop of - 10.7 mIOU. In this paper, we focus upon single source domain generalization to overcome the domain gap and propose a two-step framework wherein we first identify an adversarial style that maximizes the domain gap between stylized and source images. Subsequently, these stylized images are used to categorically align features such that features belonging to the same class are clustered together in latent space, irrespective of domain gap. Furthermore, to increase intra-class variance while training, we propose a style mixing mechanism wherein the same objects from different styles are mixed to construct a new training image. This framework allows us to achieve a domain generalized semantic segmentation algorithm with consistent performance without prior information of the target domain while relying on a single source. Based on extensive experiments, we match SoTA performance on SYNTHIA $\to$ Cityscapes, GTAV $\to$ Cityscapes while setting new SoTA on GTAV $\to$ Dark Zurich and GTAV $\to$ Night Driving benchmarks without retraining.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Event-guided Deblurring of Unknown Exposure Time Videos

Motion deblurring is a highly ill-posed problem due to the loss of motion information in the blur degradation process. Since event cameras can capture apparent motion with a high temporal resolution, several attempts have explored the potential of events for guiding deblurring. These methods generally assume that the exposure time is the same as the reciprocal of the video frame rate. However, this is not true in real situations, and the exposure time might be unknown and dynamically varies depending on the video shooting environment(e.g., illumination condition). In this paper, we address the event-guided motion deblurring assuming dynamically variable unknown exposure time of the frame-based camera. To this end, we first derive a new formulation for event-guided motion deblurring by considering the exposure and readout time in the video frame acquisition process. We then propose a novel end-to-end learning framework for event-guided motion deblurring. In particular, we design a novel Exposure Time-based Event Selection(ETES) module to selectively use event features by estimating the cross-modal correlation between the features from blurred frames and the events. Moreover, we propose a feature fusion module to fuse the selected features from events and blur frames effectively. We conduct extensive experiments on various datasets and demonstrate that our method achieves state-of-the-art performance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Facial Depth and Normal Estimation using Single Dual-Pixel Camera

Many mobile manufacturers recently have adopted Dual-Pixel (DP) sensors in their flagship models for faster auto-focus and aesthetic image captures. Despite their advantages, research on their usage for 3D facial understanding has been limited due to the lack of datasets and algorithmic designs that exploit parallax in DP images. This is because the baseline of sub-aperture images is extremely narrow and parallax exists in the defocus blur region. In this paper, we introduce a DP-oriented Depth/Normal network that reconstructs the 3D facial geometry. For this purpose, we collect a DP facial data with more than 135K images for 101 persons captured with our multi-camera structured light systems. It contains the corresponding ground-truth 3D models including depth map and surface normal in metric scale. Our dataset allows the proposed matching network to be generalized for 3D facial depth/normal estimation. The proposed network consists of two novel modules: Adaptive Sampling Module and Adaptive Normal Module, which are specialized in handling the defocus blur in DP images. Finally, the proposed method achieves state-of-the-art performances over recent DP-based depth/normal estimation methods. We also demonstrate the applicability of the estimated depth/normal to face spoofing and relighting.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge

Monocular depth estimation has been extensively explored based on deep learning, yet its accuracy and generalization ability still lag far behind the stereo-based methods. To tackle this, a few recent studies have proposed to supervise the monocular depth estimation network by distilling disparity maps as proxy ground-truths. However, these studies naively distill the stereo knowledge without considering the comparative advantages of stereo-based and monocular depth estimation methods. In this paper, we propose to selectively distill the disparity maps for more reliable proxy supervision. Specifically, we first design a decoder (MaskDecoder) that learns two binary masks which are trained to choose optimally between the proxy disparity maps and the estimated depth maps for each pixel. The learned masks are then fed to another decoder (DepthDecoder) to enforce the estimated depths to learn from only the masked area in the proxy disparity maps. Additionally, a Teacher-Student module is designed to transfer the geometric knowledge of the StereoNet to the MonoNet. Extensive experiments validate our methods achieve state-of-the-art performance for self- and proxy-supervised monocular depth estimation on the KITTI dataset, even surpassing some of the semi-supervised methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks

The limited dynamic range of commercial compact camera sensors results in an inaccurate representation of scenes with varying illumination conditions, adversely affecting image quality and subsequently limiting the performance of underlying image processing algorithms. Current state-of-the-art (SoTA) convolutional neural networks (CNN) are developed as post-processing techniques to independently recover under-/over-exposed images. However, when applied to images containing real-world degradations such as glare, high-beam, color bleeding with varying noise intensity, these algorithms amplify the degradations, further degrading image quality. We propose a lightweight two-stage image enhancement algorithm sequentially balancing illumination and noise removal using frequency priors for structural guidance to overcome these limitations. Furthermore, to ensure realistic image quality, we leverage the relationship between frequency and spatial domain properties of an image and propose a Fourier spectrum-based adversarial framework (AFNet) for consistent image enhancement under varying illumination conditions. While current formulations of image enhancement are envisioned as post-processing techniques, we examine if such an algorithm could be extended to integrate the functionality of the Image Signal Processing (ISP) pipeline within the camera sensor benefiting from RAW sensor data and lightweight CNN architecture. Based on quantitative and qualitative evaluations, we also examine the practicality and effects of image enhancement techniques on the performance of common perception tasks such as object detection and semantic segmentation in varying illumination conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation

Test-time adaptation approaches have recently emerged as a practical solution for handling domain shift without access to the source domain data. In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation. We find that directly applying existing methods usually results in performance instability at test time because multi-modal input is not considered jointly. To design a framework that can take full advantage of multi-modality, where each modality provides regularized self-supervisory signals to other modalities, we propose two complementary modules within and across the modalities. First, Intra-modal Pseudolabel Generation (Intra-PG) is introduced to obtain reliable pseudo labels within each modality by aggregating information from two models that are both pre-trained on source data but updated with target data at different paces. Second, Inter-modal Pseudo-label Refinement (Inter-PR) adaptively selects more reliable pseudo labels from different modalities based on a proposed consistency scheme. Experiments demonstrate that our regularized pseudo labels produce stable self-learning signals in numerous multi-modal test-time adaptation scenarios for 3D semantic segmentation. Visit our project website at https://www.nec-labs.com/~mas/MM-TTA.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation

Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real world. To tackle this problem, we propose an unsupervised domain adaptation (UDA) for category-level object pose estimation, called UDA-COPE. Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels. We also introduce a bidirectional filtering method between the predicted normalized object coordinate space (NOCS) map and observed point cloud, to not only make our teacher network more robust to the target domain but also to provide more reliable pseudo labels for the student network training. Extensive experimental results demonstrate the effectiveness of our proposed method both quantitatively and qualitatively. Notably, without leveraging target-domain GT labels, our proposed method achieved comparable or sometimes superior performance to existing methods that depend on the GT labels.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Towards Domain Invariant Single Image Dehazing

Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should localize and recover affected regions while ensuring consistency between recovered and its neighboring regions. However owing to fixed receptive field of convolutional kernels and non uniform haze distribution, assuring consistency between regions is difficult. In this paper, we utilize an encoder-decoder based network architecture to perform the task of dehazing and integrate an spatially aware channel attention mechanism to enhance features of interest beyond the receptive field of traditional conventional kernels. To ensure performance consistency across diverse range of haze densities, we utilize greedy localized data augmentation mechanism. Synthetic datasets are typically used to ensure a large amount of paired training samples, however the methodology to generate such samples introduces a gap between them and real images while accounting for only uniform haze distribution and overlooking more realistic scenario of non-uniform haze distribution resulting in inferior dehazing performance when evaluated on real datasets. Despite this, the abundance of paired samples within synthetic datasets cannot be ignored. Thus to ensure performance consistency across diverse datasets, we train the proposed network within an adversarial prior-guided framework that relies on a generated image along with its low and high frequency components to determine if properties of dehazed images matches those of ground truth. We preform extensive experiments to validate the dehazing and domain invariance performance of proposed framework across diverse domains and report state-of-the-art (SoTA) results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

CoAug-MR: An MR-based Interactive Office Workstation Design System via Augmented Multi-Person Collaboration

Digital prototyping and evaluation using 3D modeling and digital human models are becoming more practical for customizing products to the preference of a user. However, the 3D modeling is less accessible to casual users, and digital human models suffer from insufficient body data and less intuitive illustration on how people use the product or how it accommodates to their body. Recently, VR-supported 'Do It Yourself' design has achieved real-time ergonomic evaluation with users themselves by capturing their poses, however, it lacks reliability and quality of design. In this paper, we explore a multi-person interactive design approach that enables designers, users, and even ergonomists to collaborate to achieve effective and reliable design and prototyping tasks. Mixed Reality that utilizes Hololens and motion tracking devices had been developed to provide instant design feedback and evaluation and to experience prototyping in physical space. We evaluate the system based on the usability study, where casual users and designers are engaged in the interactive process of designing items with respect to the body information, the preference, and the environment.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations

Deep neural networks (DNNs) have achieved impressive performance on handling computer vision problems, however, it has been found that DNNs are vulnerable to adversarial examples. For such reason, adversarial perturbations have been recently studied in several respects. However, most previous works have focused on image classification tasks, and it has never been studied regarding adversarial perturbations on Image-to-image (Im2Im) translation tasks, showing great success in handling paired and/or unpaired mapping problems in the field of autonomous driving and robotics. This paper examines different types of adversarial perturbations that can fool Im2Im frameworks for autonomous driving purpose. We propose both quasi-physical and digital adversarial perturbations that can make Im2Im models yield unexpected results. We then empirically analyze these perturbations and show that they generalize well under both paired for image synthesis and unpaired settings for style transfer. We also validate that there exist some perturbation thresholds over which the Im2Im mapping is disrupted or impossible. The existence of these perturbations reveals that there exist crucial weaknesses in Im2Im models. Lastly, we show that our methods illustrate how perturbations affect the quality of outputs, pioneering the improvement of the robustness of current SOTA networks for autonomous driving.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning

Event cameras sense intensity changes and have many advantages over conventional cameras. To take advantage of event cameras, some methods have been proposed to reconstruct intensity images from event streams. However, the outputs are still in low resolution (LR), noisy, and unrealistic. The low-quality outputs stem broader applications of event cameras, where high spatial resolution (HR) is needed as well as high temporal resolution, dynamic range, and no motion blur. We consider the problem of reconstructing and super-resolving intensity images from LR events, when no ground truth (GT) HR images and down-sampling kernels are available. To tackle the challenges, we propose a novel end-to-end pipeline that reconstructs LR images from event streams, enhances the image qualities and upsamples the enhanced images, called EventSR. For the absence of real GT images, our method is primarily unsupervised, deploying adversarial learning. To train EventSR, we create an open dataset including both real-world and simulated scenes. The use of both datasets boosts up the network performance, and the network architectures and various loss functions in each phase help improve the image qualities. The whole pipeline is trained in three phases. While each phase is mainly for one of the three tasks, the networks in earlier phases are fine-tuned by respective loss functions in an end-to-end manner. Experimental results show that EventSR reconstructs high-quality SR images from events for both simulated and real-world data.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Learning to Super Resolve Intensity Images from Events

An event camera detects per-pixel intensity difference and produces asynchronous event stream with low latency, high dynamic range, and low power consumption. As a trade-off, the event camera has low spatial resolution. We propose an end-to-end network to reconstruct high resolution, high dynamic range (HDR) images directly from the event stream. We evaluate our algorithm on both simulated and real-world sequences and verify that it captures fine details of a scene and outperforms the combination of the state-of-the-art event to image algorithms with the state-of-the-art super resolution schemes in many quantitative measures by large margins. We further extend our method by using the active sensor pixel (APS) frames or reconstructing images iteratively.

0 citations0 reviewsNo code linkedOpen access