13 citations · 18 across the 8 of their papers we have counts for
8 papers · 1 filter
Mitigating State Aliasing in Vision-Language-Action Models via Inverse Dynamics Learning
Kyujin Lee, Injae Kim, Jihwan Park +3
Vision-Language-Action (VLA) models have emerged as a promising framework that unifies perception, reasoning, and control for robot manipulation by adapting pretrained vision-langu…
F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting
Injae Kim, Chaehyeon Kim, Minseong Bae +2
Feed-forward 3D Gaussian Splatting methods enable single-pass reconstruction and real-time rendering. However, they typically adopt rigid pixel-to-Gaussian or voxel-to-Gaussian pip…
Efficient multi-view training for 3D Gaussian Splatting
Minhyuk Choi, Injae Kim, Hyunwoo J. Kim
3D Gaussian Splatting (3DGS) has emerged as a preferred choice alongside Neural Radiance Fields (NeRF) in inverse rendering due to its superior rendering speed. Currently, the comm…
Robust Multimodal 3D Object Detection via Modality-Agnostic Decoding and Proximity-based Modality Ensemble
Juhan Cha, Minseok Joo, Jihwan Park +3
Recent advancements in 3D object detection have benefited from multi-modal information from the multi-view cameras and LiDAR sensors. However, the inherent disparities between the…
UP-NeRF: Unconstrained Pose-Prior-Free Neural Radiance Fields
Injae Kim, Minhyuk Choi, Hyunwoo J. Kim
Neural Radiance Field (NeRF) has enabled novel view synthesis with high fidelity given images and camera poses. Subsequent works even succeeded in eliminating the necessity of pose…
Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification
Injae Kim, Jongha Kim, Joonmyung Choi +1
Interpretability is a crucial factor in building reliable models for various medical applications. Concept Bottleneck Models (CBMs) enable interpretable image classification by uti…
Semantic-aware Occlusion Filtering Neural Radiance Fields in the Wild
Jaewon Lee, Injae Kim, Hwan Heo +1
We present a learning framework for reconstructing neural scene representations from a small number of unconstrained tourist photos. Since each image contains transient occluders,…
SageMix: Saliency-Guided Mixup for Point Clouds
Sanghyeok Lee, Minkyu Jeon, Injae Kim +2
Data augmentation is key to improving the generalization ability of deep learning models. Mixup is a simple and widely-used data augmentation technique that has proven effective in…