39 citations · 63 across the 24 of their papers we have counts for
24 papers
Reason in the Words You Speak: Idiolectal Paraphrasing Off-Policy Traces for Reasoning Distillation in VideoLLMs
Ji Soo Lee, Jinyoung Park, Seohyun Lee +4
Recent large language models achieve strong performance on complex reasoning tasks, where reinforcement learning with Group Relative Policy Optimization (GRPO) has emerged as a lea…
Context Blindness in DPO: Mitigating Object Hallucination in MLLMs via Context-Calibrated Preference Optimization
Byungoh Ko, Jinyoung Park, Jongha Kim +3
Multimodal large language models (MLLMs) have made rapid progress, yet they still exhibit object hallucination, generating plausible but incorrect descriptions that are inconsisten…
RegFormer: Transferable Relational Grounding for Efficient Weakly-Supervised Human-Object Interaction Detection
Jihwan Park, Chanhyeong Yang, Jinyoung Park +2
Weakly-supervised Human-Object Interaction (HOI) detection is essential for scalable scene understanding, as it learns interactions from only image-level annotations. Due to the la…
MoE-GRPO: Optimizing Mixture-of-Experts via Reinforcement Learning in Vision-Language Models
Dohwan Ko, Jinyoung Park, Seoung Choi +3
Mixture-of-Experts (MoE) has emerged as an effective approach to reduce the computational overhead of Transformer architectures by sparsely activating a subset of parameters for ea…
Improving Large Molecular Language Model via Relation-aware Multimodal Collaboration
Jinyoung Park, Minseong Bae, Jeehye Na +1
Large language models (LLMs) have demonstrated their instruction-following capabilities and achieved powerful performance on various tasks. Inspired by their success, recent works…
HyperGraphPro: Progress-Aware Reinforcement Learning for Structure-Guided Hypergraph RAG
Jinyoung Park, Sanghyeok Lee, Omar Zia Khan +2
Graph Retrieval-Augmented Generation (GraphRAG) has emerged as a promising paradigm that organizes external knowledge into structured graphs of entities and relations, enabling lar…
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO
Jinyoung Park, Jeehye Na, Jinyoung Kim +1
Recent works have demonstrated the effectiveness of reinforcement learning (RL)-based post-training for enhancing the reasoning capabilities of large language models (LLMs). In par…
Latent Bayesian Optimization via Autoregressive Normalizing Flows
Seunghun Lee, Jinyoung Park, Jaewon Chu +2
Bayesian Optimization (BO) has been recognized for its effectiveness in optimizing expensive and complex objective functions. Recent advancements in Latent Bayesian Optimization (L…
VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning
Ji Soo Lee, Jongha Kim, Jeehye Na +2
Despite the advancements of Video Large Language Models (VideoLLMs) in various tasks, they struggle with fine-grained temporal understanding, such as Dense Video Captioning (DVC).…
Inversion-based Latent Bayesian Optimization
Jaewon Chu, Jinyoung Park, Seunghun Lee +1
Latent Bayesian optimization (LBO) approaches have successfully adopted Bayesian optimization over a continuous latent space by employing an encoder-decoder architecture to address…
LLaMo: Large Language Model-based Molecular Graph Assistant
Jinyoung Park, Minseong Bae, Dohwan Ko +1
Large Language Models (LLMs) have demonstrated remarkable generalization and instruction-following capabilities with instruction tuning. The advancements in LLMs and instruction tu…
Generative Subgraph Retrieval for Knowledge Graph-Grounded Dialog Generation
Jinyoung Park, Minseok Joo, Joo-Kyung Kim +1
Knowledge graph-grounded dialog generation requires retrieving a dialog-relevant subgraph from the given knowledge base graph and integrating it with the dialog history. Previous w…
Prompt Learning via Meta-Regularization
Jinyoung Park, Juyeon Ko, Hyunwoo J. Kim
Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently, prompt learning approaches have b…
Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection
Jongha Kim, Jihwan Park, Jinyoung Park +3
Visual Relationship Detection (VRD) has seen significant advancements with Transformer-based architectures recently. However, we identify two key limitations in a conventional labe…
Graph Elicitation for Guiding Multi-Step Reasoning in Large Language Models
Jinyoung Park, Ameen Patel, Omar Zia Khan +2
Chain-of-Thought (CoT) prompting along with sub-question generation and answering has enhanced multi-step reasoning capabilities of Large Language Models (LLMs). However, prompting…
Sketch-based Video Object Localization
Sangmin Woo, So-Yeong Jeon, Jinyoung Park +3
We introduce Sketch-based Video Object Localization (SVOL), a new task aimed at localizing spatio-temporal object boxes in video queried by the input sketch. We first outline the c…
Self-positioning Point-based Transformer for Point Cloud Understanding
Jinyoung Park, Sanghyeok Lee, Sihyeon Kim +2
Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to…
Relation-Aware Language-Graph Transformer for Question Answering
Jinyoung Park, Hyeong Kyu Choi, Juyeon Ko +5
Question Answering (QA) is a task that entails reasoning over natural language contexts, and many relevant works augment language models (LMs) with graph neural networks (GNNs) to…
Deformable Graph Transformer
Jinyoung Park, Seongjun Yun, Hyeonjin Park +5
Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the succes…
Metropolis-Hastings Data Augmentation for Graph Neural Networks
Hyeonjin Park, Seunghun Lee, Sihyeon Kim +5
Graph Neural Networks (GNNs) often suffer from weak-generalization due to sparsely labeled data despite their promising results on various graph-based tasks. Data augmentation is a…
Deformable Graph Convolutional Networks
Jinyoung Park, Sungdong Yoo, Jihwan Park +1
Graph neural networks (GNNs) have significantly improved the representation power for graph-structured data. Despite of the recent success of GNNs, the graph convolution in most GN…
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
In recent years, graph neural networks (GNNs) have been widely adopted in the representation learning of graph-structured data and provided state-of-the-art performance in various…
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods
Byungjoo Kim, Bryce Chudomelka, Jinyoung Park +3
Deep neural networks have achieved state-of-the-art performance in a variety of fields. Recent works observe that a class of widely used neural networks can be viewed as the Euler…
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
Graph neural networks have shown superior performance in a wide range of applications providing a powerful representation of graph-structured data. Recent works show that the repre…