Congenital heart defect (CHD) detection in ultrasound videos is hindered by
image noise and probe positioning variability. While automated methods can
reduce operator dependence, current machine learning approaches often neglect
temporal informatio... read more
Tree instance segmentation of airborne laser scanning (ALS) data is of utmost
importance for forest monitoring, but remains challenging due to variations in
the data caused by factors such as sensor resolution, vegetation state at
acquisition time,... read more
We present Waver, a high-performance foundation model for unified image and
video generation. Waver can directly generate videos with durations ranging
from 5 to 10 seconds at a native resolution of 720p, which are subsequently
upscaled to 1080p. T... read more
Mesh models have become increasingly accessible for numerous cities; however,
the lack of realistic textures restricts their application in virtual urban
navigation and autonomous driving. To address this, this paper proposes MeSS
(Meshbased Scene ... read more
Spinal cord hemangioblastomas are rare, benign, intradural tumors that, despite their nonmalignant histopathology, can lead to substantial neurological morbidity. While disparities in outcomes based on race and socioeconomic status have been well-doc... read more
3D Gaussian Splatting (3DGS) has demonstrated remarkable real-time
performance in novel view synthesis, yet its effectiveness relies heavily on
dense multi-view inputs with precisely known camera poses, which are rarely
available in real-world scen... read more
Extracting narrow roads from high-resolution remote sensing imagery remains a
significant challenge due to their limited width, fragmented topology, and
frequent occlusions. To address these issues, we propose D3FNet, a Dilated
Dual-Stream Differen... read more
Communication-efficient distributed training algorithms have received
considerable interest recently due to their benefits for training Large
Language Models (LLMs) in bandwidth-constrained settings, such as across data
centers and over the interne... read more
Accurate dose-response forecasting under sparse sampling is central to
precision pharmacotherapy. We present the Amortized In-Context Mixed-Effect
Transformer (AICMET) model, a transformer-based latent-variable framework that
unifies mechanistic co... read more
This work presents MExECON, a novel pipeline for 3D reconstruction of clothed
human avatars from sparse multi-view RGB images. Building on the single-view
method ECON, MExECON extends its capabilities to leverage multiple viewpoints,
improving geom... read more
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