9 citations · 18 across the 8 of their papers we have counts for
8 papers
VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis
Jinho Park, Youbin Kim, Hogun Park +1
Spatio-temporal reasoning is a core capability for Multimodal Large Language Models (MLLMs) operating in the real world. As such, evaluating it precisely has become an essential ch…
JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA
Hyunju Kang, Woohyun Lee, Jaewon Kim +1
Industrial anomaly detection has been significantly advanced by Large Multimodal Models (LMMs), enabling diverse human instructions beyond detection, particularly through visually…
UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
Hyunju Kang, Geonhee Han, Hogun Park
Node representation learning, such as Graph Neural Networks (GNNs), has emerged as a pivotal method in machine learning. The demand for reliable explanation generation surges, yet…
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition
Heesoo Jung, Hogun Park
Self-supervised learning (SSL) in graphs has garnered significant attention, particularly in employing Graph Neural Networks (GNNs) with pretext tasks initially designed for other…
CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoders
Jongwon Park, Heesoo Jung, Hogun Park
Recent Self-Supervised Learning (SSL) methods encapsulating relational information via masking in Graph Neural Networks (GNNs) have shown promising performance. However, most exist…
AudioGenX: Explainability on Text-to-Audio Generative Models
Hyunju Kang, Geonhee Han, Yoonjae Jeong +1
Text-to-audio generation models (TAG) have achieved significant advances in generating audio conditioned on text descriptions. However, a critical challenge lies in the lack of tra…
Enhancing Knowledge Tracing with Concept Map and Response Disentanglement
Soonwook Park, Donghoon Lee, Hogun Park
In the rapidly advancing realm of educational technology, it becomes critical to accurately trace and understand student knowledge states. Conventional Knowledge Tracing (KT) model…
Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning
Jeonghoon Kim, Heesoo Jung, Hyeju Jang +1
Multi-hop logical reasoning on knowledge graphs is a pivotal task in natural language processing, with numerous approaches aiming to answer First-Order Logic (FOL) queries. Recent…