Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Hwang, Seong Jae"'
3D point clouds are increasingly vital for applications like autonomous driving and robotics, yet the raw data captured by sensors often suffer from noise and sparsity, creating challenges for downstream tasks. Consequently, point cloud upsampling be
Externí odkaz:
http://arxiv.org/abs/2411.00432
Disentangled representation learning (DRL) aims to break down observed data into core intrinsic factors for a profound understanding of the data. In real-world scenarios, manually defining and labeling these factors are non-trivial, making unsupervis
Externí odkaz:
http://arxiv.org/abs/2410.23820
Point-based image editing enables accurate and flexible control through content dragging. However, the role of text embedding in the editing process has not been thoroughly investigated. A significant aspect that remains unexplored is the interaction
Externí odkaz:
http://arxiv.org/abs/2407.17843
Reducing scan time in Positron Emission Tomography (PET) imaging while maintaining high-quality images is crucial for minimizing patient discomfort and radiation exposure. Due to the limited size of datasets and distribution discrepancy across scanne
Externí odkaz:
http://arxiv.org/abs/2407.07517
In neuroimaging, generally, brain CT is more cost-effective and accessible imaging option compared to MRI. Nevertheless, CT exhibits inferior soft-tissue contrast and higher noise levels, yielding less precise structural clarity. In response, leverag
Externí odkaz:
http://arxiv.org/abs/2407.05059
Image dehazing, addressing atmospheric interference like fog and haze, remains a pervasive challenge crucial for robust vision applications such as surveillance and remote sensing under adverse visibility. While various methodologies have evolved fro
Externí odkaz:
http://arxiv.org/abs/2407.00972
Significant methodological strides have been made toward Chest X-ray (CXR) understanding via modern vision-language models (VLMs), demonstrating impressive Visual Question Answering (VQA) and CXR report generation abilities. However, existing CXR und
Externí odkaz:
http://arxiv.org/abs/2403.15456
Recent advances in text-conditioned image generation diffusion models have begun paving the way for new opportunities in modern medical domain, in particular, generating Chest X-rays (CXRs) from diagnostic reports. Nonetheless, to further drive the d
Externí odkaz:
http://arxiv.org/abs/2403.06516
Semantic segmentation has innately relied on extensive pixel-level annotated data, leading to the emergence of unsupervised methodologies. Among them, leveraging self-supervised Vision Transformers for unsupervised semantic segmentation (USS) has bee
Externí odkaz:
http://arxiv.org/abs/2403.01482
Leveraging semantically precise pseudo masks derived from image-level class knowledge for segmentation, namely image-level Weakly Supervised Semantic Segmentation (WSSS), still remains challenging. While Class Activation Maps (CAMs) using CNNs have s
Externí odkaz:
http://arxiv.org/abs/2403.08801