12 citations · 22 across the 12 of their papers we have counts for
13 papers
Disentangling Spurious Correlations in Vision-Language-Action Models via Predicting Domain-Invariant Latent Lookahead
Junghyun Kim, Ngseo Kim, ChungWoo Lee +7
Vision-Language-Action (VLA) models remain brittle under visual distribution shifts, often relying on spurious correlations tied to domain-specific factors rather than task-relevan…
CLIP-RT: Learning Language-Conditioned Robotic Policies from Natural Language Supervision
Gi-Cheon Kang, Junghyun Kim, Kyuhwan Shim +2
Teaching robots desired skills in real-world environments remains challenging, especially for non-experts. A key bottleneck is that collecting robotic data often requires expertise…
Zero-Shot Vision-and-Language Navigation with Collision Mitigation in Continuous Environment
Seongjun Jeong, Gi-Cheon Kang, Joochan Kim +1
We propose the zero-shot Vision-and-Language Navigation with Collision Mitigation (VLN-CM), which takes these considerations. VLN-CM is composed of four modules and predicts the di…
Socratic Planner: Self-QA-Based Zero-Shot Planning for Embodied Instruction Following
Suyeon Shin, Sujin jeon, Junghyun Kim +2
Embodied Instruction Following (EIF) is the task of executing natural language instructions by navigating and interacting with objects in interactive environments. A key challenge…
Continual Vision-and-Language Navigation
Seongjun Jeong, Gi-Cheon Kang, Seongho Choi +2
Developing Vision-and-Language Navigation (VLN) agents typically assumes a \textit{train-once-deploy-once} strategy, which is unrealistic as deployed agents continually encounter n…
PGA: Personalizing Grasping Agents with Single Human-Robot Interaction
Junghyun Kim, Gi-Cheon Kang, Jaein Kim +3
Language-Conditioned Robotic Grasping (LCRG) aims to develop robots that comprehend and grasp objects based on natural language instructions. While the ability to understand person…
PROGrasp: Pragmatic Human-Robot Communication for Object Grasping
Gi-Cheon Kang, Junghyun Kim, Jaein Kim +1
Interactive Object Grasping (IOG) is the task of identifying and grasping the desired object via human-robot natural language interaction. Current IOG systems assume that a human u…
GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic Manipulation
Junghyun Kim, Gi-Cheon Kang, Jaein Kim +2
Language-Guided Robotic Manipulation (LGRM) is a challenging task as it requires a robot to understand human instructions to manipulate everyday objects. Recent approaches in LGRM…
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
Gi-Cheon Kang, Sungdong Kim, Jin-Hwa Kim +2
Visual dialog (VisDial) is a task of answering a sequence of questions grounded in an image, using the dialog history as context. Prior work has trained the dialog agents solely on…
Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering
Ahjeong Seo, Gi-Cheon Kang, Joonhan Park +1
Video Question Answering is a task which requires an AI agent to answer questions grounded in video. This task entails three key challenges: (1) understand the intention of various…
Label Propagation Adaptive Resonance Theory for Semi-supervised Continuous Learning
Taehyeong Kim, Injune Hwang, Gi-Cheon Kang +3
Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the oppo…
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
Gi-Cheon Kang, Junseok Park, Hwaran Lee +2
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context. In this paper, we study how to address two fundament…
Dual Attention Networks for Visual Reference Resolution in Visual Dialog
Gi-Cheon Kang, Jaeseo Lim, Byoung-Tak Zhang
Visual dialog (VisDial) is a task which requires an AI agent to answer a series of questions grounded in an image. Unlike in visual question answering (VQA), the series of question…