From the 3 of 170 papers with an AI index.
38 citations
- Sungkyunkwan UniversityKR60 papers
- Massachusetts Institute of TechnologyUS57 papers
- University of Maryland, College ParkUS57 papers
- Yonsei UniversityKR57 papers
- California Institute of TechnologyUS56 papers
- Rutherford Appleton LaboratoryGB56 papers
- Tsinghua UniversityCN56 papers
- Cornell UniversityUS55 papers
- University of California SystemUS55 papers
- Johns Hopkins UniversityUS54 papers
- Sapienza University of RomeIT54 papers
- Universidad Autónoma de MadridES54 papers
170 papers
Accretion Burst Crystallizes Silicates in a Planet-Forming Disk
Jeong-Eun Lee, Chul-Hwan Kim, Jaeyeong Kim +13
The paper reports JWST mid‑infrared observations of the bursting protostar EC 53 that reveal crystalline silicate emission appearing only during an accretion burst, providing direc…
CDIS: Cross-Dimensional Class-Agnostic 3D Instance Segmentation via 2D Mask Tracking and 3D-2D Projection Merging
Juno Kim, Hye-Jung Yoon, Yesol Park +1
Class-agnostic 3D instance segmentation is critical for robotic systems operating in unknown environments, enabling perception of previously unseen objects for reliable manipulatio…
Seg2Grasp: A Robust Modular Suction Grasping in Bin Picking
Hye-Jung Yoon, Juno Kim, Yesol Park +2
Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments. To overcome these…
DA-Fusion: Deformable Attention-Based RGB-D Fusion Transformer for Unseen Object Instance Segmentation
Yesol Park, Hye-Jung Yoon, Juno Kim +1
In logistics automation, precise segmentation of unseen objects is crucial for efficient robotic manipulation in cluttered environments. Tasks such as bin-picking and shelf-picking…
LoSA-Net: A Localized and Scale-Adaptive Network for Boundary-Sensitive Prediction of Perineural Invasion in 3D MRI
Youngung Han, Hyunsu Go, Kyeonghun Kim +9
The paper introduces LoSA-Net, a neural network that uses localized self‑attention and scale‑adaptive processing to improve detection of perineural invasion boundaries in 3D contra…
MMA-Former: Multi-Window Mixture-of-Head Attention Transformer for Adaptive PNI Prediction in 3D MRI
Youngung Han, Induk Um, Kyeonghun Kim +9
The paper introduces MMA-Former, a 3D transformer model with a multi-window mixture-of-head attention mechanism, to predict perineural invasion from T1-weighted MRI scans.