arXiv subjects

Sanghyun Kim

Publications and source records attributed to Sanghyun Kim.

At least 37 records · Page 2Linked to original sources

arXiv2025

Few-Shot Pattern Detection via Template Matching and Regression

We address the problem of few-shot pattern detection, which aims to detect all instances of a given pattern, typically represented by a few exemplars, from an input image. Although similar problems have been studied in few-shot object counting and detection (FSCD), previous methods and their benchmarks have narrowed patterns of interest to object categories and often fail to localize non-object patterns. In this work, we propose a simple yet effective detector based on template matching and regression, dubbed TMR. While previous FSCD methods typically represent target exemplars as spatially collapsed prototypes and lose structural information, we revisit classic template matching and regression. It effectively preserves and leverages the spatial layout of exemplars through a minimalistic structure with a small number of learnable convolutional or projection layers on top of a frozen backbone We also introduce a new dataset, dubbed RPINE, which covers a wider range of patterns than existing object-centric datasets. Our method outperforms the state-of-the-art methods on the three benchmarks, RPINE, FSCD-147, and FSCD-LVIS, and demonstrates strong generalization in cross-dataset evaluation.

cs.CV↗

arXiv2025

Unveiling blazar synchrotron emission: a multiwavelength polarimetric study of HSP and LSP populations

Polarimetric properties of blazars allow us to put constraints on the acceleration mechanisms that fuel their powerful jets. By studying the multiwavelength polarimetric behaviour of high synchrotron peaked (HSP) and low synchrotron peaked (LSP) blazars, we aim to explore differences in their emission mechanisms and magnetic field structure in the acceleration region. In this study, we take advantage of several X-ray polarisation observations of HSP by the IXPE, including four new observations of Mrk 501, and optical polarisation observations of LSP from RoboPol and many others. We find that the polarisation degree (PD) distribution of HSP in X-rays is systematically higher than in optical and mm-radio wavelengths, as reported in previous IXPE publications. The distribution of the X-ray electric vector position angles (PA) is centered around the jet axis with most of the observations consistent with zero difference within uncertainties. In fact, the distribution of the offset of the PA from the jet axis is consistent between the LSP and HSP populations (with PA measured in optical for the first, X-ray for the latter), suggesting a common magnetic field structure close to the acceleration region, in strong support of the emerging energy stratified picture of particle acceleration followed by energy loss in blazar jets.

astro-ph.HE↗

arXiv2025

Enhancing Urban GNSS Positioning Reliability via Conservative Satellite Selection Using Unanimous Voting Across Multiple Machine Learning Classifiers

In urban environments, global navigation satellite system (GNSS) positioning is often compromised by signal blockages and multipath effects caused by buildings, leading to significant positioning errors. To address this issue, this study proposes a robust enhancement of zonotope shadow matching (ZSM)-based positioning by employing a conservative satellite selection strategy using unanimous voting across multiple machine learning classifiers. Three distinct models - random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM) - were trained to perform line-of-sight (LOS) and non-line-of-sight (NLOS) classification based on global positioning system (GPS) signal features. A satellite is selected for positioning only when all classifiers unanimously agree on its classification and their associated confidence scores exceed a threshold. Experiments with real-world GPS data collected in dense urban areas demonstrate that the proposed method significantly improves the positioning success rate and the receiver containment rate, even with imperfect LOS/NLOS classification. Although a slight increase in the position bound was observed due to the reduced number of satellites used, overall positioning reliability was substantially enhanced, indicating the effectiveness of the proposed approach in urban GNSS environments.

eess.SP↗

arXiv2025

CSC-MPPI: A Novel Constrained MPPI Framework with DBSCAN for Reliable Obstacle Avoidance

This paper proposes Constrained Sampling Cluster Model Predictive Path Integral (CSC-MPPI), a novel constrained formulation of MPPI designed to enhance trajectory optimization while enforcing strict constraints on system states and control inputs. Traditional MPPI, which relies on a probabilistic sampling process, often struggles with constraint satisfaction and generates suboptimal trajectories due to the weighted averaging of sampled trajectories. To address these limitations, the proposed framework integrates a primal-dual gradient-based approach and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to steer sampled input trajectories into feasible regions while mitigating risks associated with weighted averaging. First, to ensure that sampled trajectories remain within the feasible region, the primal-dual gradient method is applied to iteratively shift sampled inputs while enforcing state and control constraints. Then, DBSCAN groups the sampled trajectories, enabling the selection of representative control inputs within each cluster. Finally, among the representative control inputs, the one with the lowest cost is chosen as the optimal action. As a result, CSC-MPPI guarantees constraint satisfaction, improves trajectory selection, and enhances robustness in complex environments. Simulation and real-world experiments demonstrate that CSC-MPPI outperforms traditional MPPI in obstacle avoidance, achieving improved reliability and efficiency. The experimental videos are available at https://cscmppi.github.io

cs.RO↗

arXiv2025

EVT: Efficient View Transformation for Multi-Modal 3D Object Detection

Multi-modal sensor fusion in Bird's Eye View (BEV) representation has become the leading approach for 3D object detection. However, existing methods often rely on depth estimators or transformer encoders to transform image features into BEV space, which reduces robustness or introduces significant computational overhead. Moreover, the insufficient geometric guidance in view transformation results in ray-directional misalignments, limiting the effectiveness of BEV representations. To address these challenges, we propose Efficient View Transformation (EVT), a novel 3D object detection framework that constructs a well-structured BEV representation, improving both accuracy and efficiency. Our approach focuses on two key aspects. First, Adaptive Sampling and Adaptive Projection (ASAP), which utilizes LiDAR guidance to generate 3D sampling points and adaptive kernels, enables more effective transformation of image features into BEV space and a refined BEV representation. Second, an improved query-based detection framework, incorporating group-wise mixed query selection and geometry-aware cross-attention, effectively captures both the common properties and the geometric structure of objects in the transformer decoder. On the nuScenes test set, EVT achieves state-of-the-art performance of 75.3% NDS with real-time inference speed.

cs.CV↗

arXiv2025

Determining the origin of the X-ray emission in blazars through multiwavelength polarization

The origin of the high-energy emission in astrophysical jets from black holes is a highly debated issue. This is particularly true for jets from supermassive black holes that are among the most powerful particle accelerators in the Universe. So far, the addition of new observations and new messengers have only managed to create more questions than answers. However, the newly available X-ray polarization observations promise to finally distinguish between emission models. We use extensive multiwavelength and polarization campaigns as well as state-of-the-art polarized spectral energy distribution models to attack this problem by focusing on two X-ray polarization observations of blazar BL Lacertae in flaring and quiescent $γ$-ray states. We find that regardless of the jet composition and underlying emission model, inverse-Compton scattering from relativistic electrons dominates at X-ray energies.

astro-ph.HE↗

arXiv2025

Locality-Aware Zero-Shot Human-Object Interaction Detection

Recent methods for zero-shot Human-Object Interaction (HOI) detection typically leverage the generalization ability of large Vision-Language Model (VLM), i.e., CLIP, on unseen categories, showing impressive results on various zero-shot settings. However, existing methods struggle to adapt CLIP representations for human-object pairs, as CLIP tends to overlook fine-grained information necessary for distinguishing interactions. To address this issue, we devise, LAIN, a novel zero-shot HOI detection framework enhancing the locality and interaction awareness of CLIP representations. The locality awareness, which involves capturing fine-grained details and the spatial structure of individual objects, is achieved by aggregating the information and spatial priors of adjacent neighborhood patches. The interaction awareness, which involves identifying whether and how a human is interacting with an object, is achieved by capturing the interaction pattern between the human and the object. By infusing locality and interaction awareness into CLIP representation, LAIN captures detailed information about the human-object pairs. Our extensive experiments on existing benchmarks show that LAIN outperforms previous methods on various zero-shot settings, demonstrating the importance of locality and interaction awareness for effective zero-shot HOI detection.

cs.CV↗

arXiv2025

High optical to X-ray polarization ratio reveals Compton scattering in BL Lacertae's jet

Blazars, supermassive black hole systems (SMBHs) with highly relativistic jets aligned with the line of sight, are the most powerful long-lived emitters of electromagnetic emission in the Universe. We report here on a radio to gamma-ray multiwavelength campaign on the blazar BL Lacertae with unprecedented polarimetric coverage from radio to X-ray wavelengths. The observations caught an extraordinary event on 2023 November 10-18, when the degree of linear polarization of optical synchrotron radiation reached a record value of 47.5%. In stark contrast, the Imaging X-ray Polarimetry Explorer (IXPE) found that the X-ray (Compton scattering or hadron-induced) emission was polarized at less than 7.4% (3sigma confidence level). We argue here that this observational result rules out a hadronic origin of the high energy emission, and strongly favors a leptonic (Compton scattering) origin, thereby breaking the degeneracy between hadronic and leptonic emission models for BL Lacertae and demonstrating the power of multiwavelength polarimetry to address this question. Furthermore, the multiwavelength flux and polarization variability, featuring an extremely prominent rise and decay of the optical polarization degree, is interpreted for the first time by the relaxation of a magnetic "spring" embedded in the newly injected plasma. This suggests that the plasma jet can maintain a predominant toroidal magnetic field component parsecs away from the central engine.

astro-ph.HE↗

arXiv2025

Polarization flare of 3C 454.3 in millimeter wavelengths seen from decadal polarimetric observations

This study investigates polarimetric characteristics of the blazar 3C~454.3 at 22-129~GHz using decadal~(2011-2022) data sets. In addition, we also delve into the origin of the polarization flare observed in 2019. The data sets were obtained from the single-dish mode observations of the Korean VLBI Network~(KVN) and the 43-GHz Very Long Baseline Array~(VLBA). We compared the consistency of the measurements between milli-arcsecond and arcsecond scales. The Faraday rotation measure values were obtained via two approaches, model fitting to a linear function in all frequency ranges, and calculation from adjacent frequency pairs. We found that the linear polarization angle is preferred to be $\sim100^{\circ}$ when the source is highly polarized. At 43~GHz, we found that the polarized emission at scales of mas and arcsecond is consistent when we compare its flux density and polarization angle. The ratio of quasi-simultaneously measured polarized flux density is $1.02\pm0.07$, and the polarization angles display similar rotation. These suggest that the extended jet beyond the scale of VLBA 43~GHz has a negligible convolution effect on the polarization angle from the KVN. We found an interesting, notable flaring event in the KVN single-dish data from the polarized emission in 2019 in the frequency range of 22-129~GHz. During the flare, the observed polarization angles~($χ_{\rm obs}$) rotate from $\sim150^{\circ}$ to $\sim100^{\circ}$ at all frequencies with a chromatic polarization degree~($m_{\rm p}$). Based on the observed $m_{\rm p}$ and $χ_{\rm obs}$, and also the Faraday rotation measure, we suggest that the polarization flare in 2019 is attributed to the shock-shock interaction in the stationary jet region. The change in the viewing angle of the jet alone is insufficient to describe the increase in brightness temperature, indicating the presence of source intrinsic processes.

astro-ph.GA↗

arXiv2025

Set-Based Position Ambiguity Reduction Method for Zonotope Shadow Matching in Urban Areas Using Estimated Multipath Errors

In urban areas, the quality of global navigation satellite system (GNSS) signals deteriorates, leading to reduced positioning accuracy. To address this issue, 3D-mapping-aided (3DMA) techniques, such as shadow matching and zonotope shadow matching (ZSM), have been proposed. However, these methods can introduce a problem known as multi-modal position ambiguity, making it challenging to select the exact mode in which the receiver is located. Accurately selecting the correct mode is essential for improving positioning accuracy. A previous study proposed a method that uses satellite-pseudorange consistency (SPC), calculated from pseudorange measurements, to select the mode containing the receiver. This method achieved a mode selection accuracy of approximately 78%. To further enhance accuracy, the study utilized pseudorange measurements collected at multiple timesteps from a fixed location and a trained line-of-sight (LOS) classifier. However, in practice, collecting data at multiple timesteps from the same location in dynamic environments is challenging. Moreover, the performance of the trained LOS classifier heavily depends on the surrounding environment, leading to low reliability. In this study, we propose a method that estimates and corrects multipath errors based on the mode distribution obtained from the output of ZSM and extract an enhanced SPC using the corrected pseudorange measurements. This enables high mode selection accuracy using only single-timestep pseudorange measurements, without requiring a trained LOS classifier.

eess.SP↗

arXiv2025

Gamma-ray flares from the jet of the blazar CTA 102 in 2016-2018

CTA 102 is a $γ$-ray bright blazar that exhibited multiple flares in observations by the Large Area Telescope on board the Fermi Gamma-Ray Space Telescope during the period of 2016-2018. We present results from the analysis of multi-wavelength light curves aiming at revealing the nature of $γ$-ray flares from the relativistic jet in the blazar. We analyse radio, optical, X-ray, and $γ$-ray data obtained in a period from 2012 September 29 to 2018 October 8. We identify six flares in the $γ$-ray light curve, showing a harder-when-brighter-trend in the $γ$-ray spectra. We perform a cross-correlation analysis of the multi-wavelength light curves. We find nearly zero time lags between the $γ$-ray and optical and X-ray light curves, implying a common spatial origin for the emission in these bands. We find significant correlations between the $γ$-ray and radio light curves as well as negative/positive time lags with the $γ$-ray emission lagging/leading the radio during different flaring periods. The time lags between $γ$-ray and radio emission propose the presence of multiple $γ$-ray emission sites in the source. As seen in 43 GHz images from the Very Long Baseline Array, two moving disturbances (or shocks) were newly ejected from the radio core. The $γ$-ray flares from 2016 to 2017 are temporally coincident with the interaction between a traveling shock and a quasi-stationary one at $\sim$0.1 mas from the core. The other shock is found to emerge from the core nearly simultaneous with the $γ$-ray flare in 2018. Our results suggest that the $γ$-ray flares originated from shock-shock interactions.

astro-ph.HE↗

arXiv2024

Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models

Fine-tuning text-to-image diffusion models is widely used for personalization and adaptation for new domains. In this paper, we identify a critical vulnerability of fine-tuning: safety alignment methods designed to filter harmful content (e.g., nudity) can break down during fine-tuning, allowing previously suppressed content to resurface, even when using benign datasets. While this "fine-tuning jailbreaking" issue is known in large language models, it remains largely unexplored in text-to-image diffusion models. Our investigation reveals that standard fine-tuning can inadvertently undo safety measures, causing models to relearn harmful concepts that were previously removed and even exacerbate harmful behaviors. To address this issue, we present a novel but immediate solution called Modular LoRA, which involves training Safety Low-Rank Adaptation (LoRA) modules separately from Fine-Tuning LoRA components and merging them during inference. This method effectively prevents the re-learning of harmful content without compromising the model's performance on new tasks. Our experiments demonstrate that Modular LoRA outperforms traditional fine-tuning methods in maintaining safety alignment, offering a practical approach for enhancing the security of text-to-image diffusion models against potential attacks.

cs.AI↗

arXiv2024

Quantum Support Vector Machine-Based Classification of GPS Signal Reception Conditions

Global Positioning System (GPS) plays a critical role in navigation by utilizing satellite signals, but its accuracy in urban environments is often compromised by signal obstructions. Previous research has categorized GPS reception conditions into line-of-sight (LOS), non-line-of-sight (NLOS), and LOS+NLOS scenarios to enhance accuracy. This paper introduces a novel approach using quantum support vector machines (QSVM) with a ZZ feature map and fidelity quantum kernel to classify urban GPS signal reception conditions, comparing its performance against classical SVM methods. While classical SVM has been previously explored for this purpose, our study is the first to apply QSVM to this classification task. We conducted experiments using datasets from two distinct urban locations to train and evaluate SVM and QSVM models. Our results demonstrate that QSVM achieves superior classification accuracy compared to classical SVM for urban GPS signal datasets. Additionally, we emphasize the importance of appropriately scaling raw data when utilizing QSVM.

eess.SP↗

arXiv2024

Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion

This paper addresses the societal concerns arising from large-scale text-to-image diffusion models for generating potentially harmful or copyrighted content. Existing models rely heavily on internet-crawled data, wherein problematic concepts persist due to incomplete filtration processes. While previous approaches somewhat alleviate the issue, they often rely on text-specified concepts, introducing challenges in accurately capturing nuanced concepts and aligning model knowledge with human understandings. In response, we propose a framework named Human Feedback Inversion (HFI), where human feedback on model-generated images is condensed into textual tokens guiding the mitigation or removal of problematic images. The proposed framework can be built upon existing techniques for the same purpose, enhancing their alignment with human judgment. By doing so, we simplify the training objective with a self-distillation-based technique, providing a strong baseline for concept removal. Our experimental results demonstrate our framework significantly reduces objectionable content generation while preserving image quality, contributing to the ethical deployment of AI in the public sphere.

cs.CV↗

arXiv2024

Burst Image Super-Resolution with Base Frame Selection

Burst image super-resolution has been a topic of active research in recent years due to its ability to obtain a high-resolution image by using complementary information between multiple frames in the burst. In this work, we explore using burst shots with non-uniform exposures to confront real-world practical scenarios by introducing a new benchmark dataset, dubbed Non-uniformly Exposed Burst Image (NEBI), that includes the burst frames at varying exposure times to obtain a broader range of irradiance and motion characteristics within a scene. As burst shots with non-uniform exposures exhibit varying levels of degradation, fusing information of the burst shots into the first frame as a base frame may not result in optimal image quality. To address this limitation, we propose a Frame Selection Network (FSN) for non-uniform scenarios. This network seamlessly integrates into existing super-resolution methods in a plug-and-play manner with low computational costs. The comparative analysis reveals the effectiveness of the nonuniform setting for the practical scenario and our FSN on synthetic-/real- NEBI datasets.

cs.CV↗

arXiv2024

Online Multi-Contact Receding Horizon Planning via Value Function Approximation

Planning multi-contact motions in a receding horizon fashion requires a value function to guide the planning with respect to the future, e.g., building momentum to traverse large obstacles. Traditionally, the value function is approximated by computing trajectories in a prediction horizon (never executed) that foresees the future beyond the execution horizon. However, given the non-convex dynamics of multi-contact motions, this approach is computationally expensive. To enable online Receding Horizon Planning (RHP) of multi-contact motions, we find efficient approximations of the value function. Specifically, we propose a trajectory-based and a learning-based approach. In the former, namely RHP with Multiple Levels of Model Fidelity, we approximate the value function by computing the prediction horizon with a convex relaxed model. In the latter, namely Locally-Guided RHP, we learn an oracle to predict local objectives for locomotion tasks, and we use these local objectives to construct local value functions for guiding a short-horizon RHP. We evaluate both approaches in simulation by planning centroidal trajectories of a humanoid robot walking on moderate slopes, and on large slopes where the robot cannot maintain static balance. Our results show that locally-guided RHP achieves the best computation efficiency (95\%-98.6\% cycles converge online). This computation advantage enables us to demonstrate online receding horizon planning of our real-world humanoid robot Talos walking in dynamic environments that change on-the-fly.

cs.RO↗

arXiv2024

Results and Lessons Learned from Autonomous Driving Transportation Services in Airfield, Crowded Indoor, and Urban Environments

Autonomous vehicles have been actively investigated over the past few decades. Several recent works show the potential of autonomous vehicles in urban environments with impressive experimental results. However, these works note that autonomous vehicles are still occasionally inferior to expert drivers in complex scenarios. Furthermore, they do not focus on the possibilities of autonomous driving transportation services in other areas beyond urban environments. This paper presents the research results and lessons learned from autonomous driving transportation services in airfield, crowded indoor, and urban environments. We discuss how we address several unique challenges in these diverse environments. We also offer an overview of remaining challenges that have not received much attention but must be addressed. This paper aims to share our unique experience to support researchers who are interested in exploring autonomous driving transportation services in various real-world environments.

cs.RO↗

arXiv2023

Slot-Mixup with Subsampling: A Simple Regularization for WSI Classification

Whole slide image (WSI) classification requires repetitive zoom-in and out for pathologists, as only small portions of the slide may be relevant to detecting cancer. Due to the lack of patch-level labels, multiple instance learning (MIL) is a common practice for training a WSI classifier. One of the challenges in MIL for WSIs is the weak supervision coming only from the slide-level labels, often resulting in severe overfitting. In response, researchers have considered adopting patch-level augmentation or applying mixup augmentation, but their applicability remains unverified. Our approach augments the training dataset by sampling a subset of patches in the WSI without significantly altering the underlying semantics of the original slides. Additionally, we introduce an efficient model (Slot-MIL) that organizes patches into a fixed number of slots, the abstract representation of patches, using an attention mechanism. We empirically demonstrate that the subsampling augmentation helps to make more informative slots by restricting the over-concentration of attention and to improve interpretability. Finally, we illustrate that combining our attention-based aggregation model with subsampling and mixup, which has shown limited compatibility in existing MIL methods, can enhance both generalization and calibration. Our proposed methods achieve the state-of-the-art performance across various benchmark datasets including class imbalance and distribution shifts.

cs.CV↗