52 citations · 64 across the 5 of their papers we have counts for
6 papers
FlowVLA: Enabling World Dynamics Understanding through Pluggable 3D Flow Learning
Junhyeong Go, Mingyu Jung, Woobin Im +1
Recent advances in 3D reconstruction enable the extraction of temporally consistent 3D motion from RGB observations; however, current Vision-Language-Action (VLA) models still pred…
Gramian Attention Heads are Strong yet Efficient Vision Learners
Jongbin Ryu, Dongyoon Han, Jongwoo Lim
We introduce a novel architecture design that enhances expressiveness by incorporating multiple head classifiers (\ie, classification heads) instead of relying on channel expansion…
Spatial Bias for Attention-free Non-local Neural Networks
Junhyung Go, Jongbin Ryu
In this paper, we introduce the spatial bias to learn global knowledge without self-attention in convolutional neural networks. Owing to the limited receptive field, conventional c…
Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation
Jongbin Ryu, Jiun Bae, Jongwoo Lim
In this paper, we introduce a collaborative training algorithm of balanced random forests with convolutional neural networks for domain adaptation tasks. In real scenarios, most do…
OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching
Changhee Won, Jongbin Ryu, Jongwoo Lim
In this paper, we propose a novel end-to-end deep neural network model for omnidirectional depth estimation from a wide-baseline multi-view stereo setup. The images captured with u…
SweepNet: Wide-baseline Omnidirectional Depth Estimation
Changhee Won, Jongbin Ryu, Jongwoo Lim
Omnidirectional depth sensing has its advantage over the conventional stereo systems since it enables us to recognize the objects of interest in all directions without any blind re…