InMu-Net: Advancing Multi-modal Intent Detection via Information Bottleneck and Multi-sensory Processing
Multi-modal intent detection (MID) aims to comprehend users' intentions through diverse
modalities, which has received widespread attention in dialogue systems. Despite the …
modalities, which has received widespread attention in dialogue systems. Despite the …
Fuzzy attention-based border rendering orthogonal network for lung organ segmentation
Automatic lung organ segmentation on computerized tomography images is crucial for lung
disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs …
disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs …
Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction
Sparse-View Computed Tomography (SVCT) offers low-dose and fast imaging but suffers
from severe artifacts. Optimizing the sampling strategy is an essential approach to improving …
from severe artifacts. Optimizing the sampling strategy is an essential approach to improving …
Fuzzy Attention-Based Border Rendering Network for Lung Organ Segmentation
Automatic lung organ segmentation on CT images is crucial for lung disease diagnosis.
However, the unlimited voxel values and class imbalance of lung organs can lead to false …
However, the unlimited voxel values and class imbalance of lung organs can lead to false …
QADM-Net: Quality-adaptive Dynamic Network for Reliable Multimodal Classification
S Shen, T Zhang, CL Chen - arxiv preprint arxiv:2412.14489, 2024 - arxiv.org
Integrating complementary information from different data modalities can yield
representation with stronger expressive ability. However, data quality varies across …
representation with stronger expressive ability. However, data quality varies across …