publications

2024

1. RAL

   Towards Embedding Dynamic Personas in Interactive Robots: Masquerading Animated Social Kinematics (MASK)

   Jeongeun Park, Taemoon Jeong,  Hyeonseong Kim and 7 more authors

   In

   Blog

2023

1. NeurIPSWorkshop

   Past as a Guide: Leveraging Retrospective Learning for Python Code Completion

   Seungyoun Shin, Seunggyu Chang, and Sungjoon Choi

   In NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following

   Abstract PDF Blog Code

   This work presents Past as a Guide (PaG), a simple approach for Large Language Models (LLMs) to improve the coding capabilities by integrating the past history with interactive and iterative code refinements. To be specific, inspired by human cognitive processes, the proposed method enables LLMs to utilize previous programming and debugging experiences to enhance the Python code completion tasks. The framework facilitates LLMs to iteratively refine the Python code based on previous execution and debugging results and optimize learning and reasoning capabilities. The proposed methodology achieved a 92% pass@1 on HumanEval, demonstrating the potential to advance the field by leveraging retrospection from past experiences and interactive and iterative refinement processes without external correctness indicators.

2. ACPR2023

   Towards Explainable Computer Vision Methods via Uncertainty Activation Map

   Seungyoun Shin, Wonho Bae,  Junhyug Noh and 1 more author

   In ACPR 2023:Pattern Recognition

   Abstract

   This paper focuses on the problem of highlighting the input image regions that result in increasing predictive uncertainty. In particular, we focus on two types of uncertainty, epistemic and aleatoric, and present an uncertainty activation mapping method that can incorporate both types of uncertainty. To this end, we first utilize a mixture-of-experts model combined with class-activation mapping (CAM). The proposed method is extensively evaluated in two different scenarios: multi-label and artificial noise injection scenarios, where we show that our proposed method can effectively capture uncertain regions

3. ACPR2023

   Robust Detection for Autonomous Elevator Boarding Using a Mobile Manipulator

   Seungyoun Shin, Joon Hyung Lee,  Junhyug Noh and 1 more author

   In ACPR 2023:Pattern Recognition

   Abstract Blog

   Indoor robots are becoming increasingly prevalent across a range of sectors, but the challenge of navigating multi-level structures through elevators remains largely uncharted. For a robot to operate successfully, it’s pivotal to have an accurate perception of elevator states. This paper presents a robust robotic system, tailored to interact adeptly with elevators by discerning their status, actuating buttons, and boarding seamlessly. Given the inherent issues of class imbalance and limited data, we utilize the YOLOv7 model and adopt specific strategies to counteract the potential decline in object detection performance. Our method effectively confronts the class imbalance and label dependency observed in real-world datasets, Our method effectively confronts the class imbalance and label dependency observed in real-world datasets, offering a promising approach to improve indoor robotic navigation systems.

2022

1. arXiv

   Learning Joint Representation of Human Motion and Language

   Jihoon Kim, Youngjae Yu,  Seungyoun Shin and 2 more authors

   arXiv:2210.15187

   PDF
2. PR

   Conditional motion in-betweening

   Jihoon Kim, Taehyun Byun,  Seungyoun Shin and 2 more authors

   Pattern Recognition

   PDF Blog
3. PR

   Elucidating robust learning with uncertainty-aware corruption pattern estimation

   Jeongeun Park, Seungyoun Shin,  Sangheum Hwang and 1 more author

   Pattern Recognition

   PDF