arXiv subjects

Jongeun Kim

Publications and source records attributed to Jongeun Kim.

7 recordsLinked to original sources

arXiv2026

CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documents

Retrieval-Augmented Generation (RAG) systems lose retrieval accuracy when similar documents coexist in the vector database, causing unnecessary information, hallucinations, and factual errors. To alleviate this issue, we propose CHOP, a framework that iteratively evaluates chunk relevance with Large Language Models (LLMs) and progressively reconstructs documents by determining their association with specific topics or query types. CHOP integrates two key components: the CNM-Extractor, which generates compact per-chunk signatures capturing categories, key nouns, and model names, and the Continuity Decision Module, which preserves contextual coherence by deciding whether consecutive chunks belong to the same document flow. By prefixing each chunk with context-aware metadata, CHOP reduces semantic conflicts among similar documents and enhances retriever discrimination. Experiments on benchmark datasets show that CHOP alleviates retrieval confusion and provides a scalable approach for building high-quality knowledge bases, achieving a Top-1 Hit Rate of 90.77% and notable gains in ranking quality metrics.

cs.CL↗

arXiv2026

Resource-Efficient RGB-Only Action Recognition for Edge Deployment

Resource-constrained assistive monitoring requires compact local video perception and an explicit understanding of how recognition reliability changes under common visual degradation. We present a compact RGB-only action-recognition network that combines an X3D-style hierarchy with temporal shift, a 3D Universal Inverted Bottleneck, Ghost pointwise convolutions, factorized depthwise operators, selective temporal adaptation, and parameter-free attention. The model attains 95.10/98.31% on NTU RGB+D 60 (X-Sub/X-View) and 90.88/92.66% on NTU RGB+D 120 (X-Sub/X-Set) with only 0.96-0.99M parameters. To connect compact deployment with assistive use, we evaluate frozen checkpoints without retraining on the datasets' official Medical Conditions groups while retaining the original 60-/120-way decision spaces. Medical macro recall reaches 93.19/97.93% on NTU60 and 91.47/91.48% on NTU120. Under controlled Level-2 visual stress on NTU60 X-Sub, medical recall drops by 1.77 points under low light, 2.23 under motion blur, 4.87 under crop perturbation, and 19.05 under occlusion; among the four selected Level-2 perturbations, occlusion causes the largest observed degradation. On Jetson Orin Nano, the TensorRT FP16 engine occupies 5.30 MiB, demonstrating strong static compactness but not throughput superiority. The results position the model as a compact perception component for lower-rate assistive monitoring, rather than as a complete clinical or robotic system.

cs.CV↗

arXiv2026

From Prompts to Deployment: Auto-Curated Domain-Specific Dataset Generation via Diffusion Models

In this paper, we present an automated pipeline for generating domain-specific synthetic datasets with diffusion models, addressing the distribution shift between pre-trained models and real-world deployment environments. Our three-stage framework first synthesizes target objects within domain-specific backgrounds through controlled inpainting. The generated outputs are then validated via a multi-modal assessment that integrates object detection, aesthetic scoring, and vision-language alignment. Finally, a user-preference classifier is employed to capture subjective selection criteria. This pipeline enables the efficient construction of high-quality, deployable datasets while reducing reliance on extensive real-world data collection.

cs.CV↗

arXiv2025

Your Super Resolution Model is not Enough for Tackling Real-World Scenarios

Despite remarkable progress in Single Image Super-Resolution (SISR), traditional models often struggle to generalize across varying scale factors, limiting their real-world applicability. To address this, we propose a plug-in Scale-Aware Attention Module (SAAM) designed to retrofit modern fixed-scale SR models with the ability to perform arbitrary-scale SR. SAAM employs lightweight, scale-adaptive feature extraction and upsampling, incorporating the Simple parameter-free Attention Module (SimAM) for efficient guidance and gradient variance loss to enhance sharpness in image details. Our method integrates seamlessly into multiple state-of-the-art SR backbones (e.g., SCNet, HiT-SR, OverNet), delivering competitive or superior performance across a wide range of integer and non-integer scale factors. Extensive experiments on benchmark datasets demonstrate that our approach enables robust multi-scale upscaling with minimal computational overhead, offering a practical solution for real-world scenarios.

cs.CV↗

arXiv2023

Effective Slogan Generation with Noise Perturbation

Slogans play a crucial role in building the brand's identity of the firm. A slogan is expected to reflect firm's vision and brand's value propositions in memorable and likeable ways. Automating the generation of slogans with such characteristics is challenging. Previous studies developted and tested slogan generation with syntactic control and summarization models which are not capable of generating distinctive slogans. We introduce a a novel apporach that leverages pre-trained transformer T5 model with noise perturbation on newly proposed 1:N matching pair dataset. This approach serves as a contributing fator in generting distinctive and coherent slogans. Turthermore, the proposed approach incorporates descriptions about the firm and brand into the generation of slogans. We evaluate generated slogans based on ROUGE1, ROUGEL and Cosine Similarity metrics and also assess them with human subjects in terms of slogan's distinctiveness, coherence, and fluency. The results demonstrate that our approach yields better performance than baseline models and other transformer-based models.

cs.CL↗

arXiv2022

Technical Report for CVPR 2022 LOVEU AQTC Challenge

This technical report presents the 2nd winning model for AQTC, a task newly introduced in CVPR 2022 LOng-form VidEo Understanding (LOVEU) challenges. This challenge faces difficulties with multi-step answers, multi-modal, and diverse and changing button representations in video. We address this problem by proposing a new context ground module attention mechanism for more effective feature mapping. In addition, we also perform the analysis over the number of buttons and ablation study of different step networks and video features. As a result, we achieved the overall 2nd place in LOVEU competition track 3, specifically the 1st place in two out of four evaluation metrics. Our code is available at https://github.com/jaykim9870/ CVPR-22_LOVEU_unipyler.

cs.CV↗

arXiv2021

Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics

In this paper we consider the problem of learning a regression function without assuming its functional form. This problem is referred to as symbolic regression. An expression tree is typically used to represent a solution function, which is determined by assigning operators and operands to the nodes. The symbolic regression problem can be formulated as a nonconvex mixed-integer nonlinear program (MINLP), where binary variables are used to assign operators and nonlinear expressions are used to propagate data values through nonlinear operators such as square, square root, and exponential. We extend this formulation by adding new cuts that improve the solution of this challenging MINLP. We also propose a heuristic that iteratively builds an expression tree by solving a restricted MINLP. We perform computational experiments and compare our approach with a mixed-integer program-based method and a neural-network-based method from the literature.

math.OC↗