376 research outputs found
Bernstein Theorems for Space-like Graphs with Parallel Mean Curvature and Controlled Growth
In this paper, we obtain an Ecker-Huisken type result for entire graphs with
parallel mean curvature.Comment: 12 page
EVA-CLIP: Improved Training Techniques for CLIP at Scale
Contrastive language-image pre-training, CLIP for short, has gained
increasing attention for its potential in various scenarios. In this paper, we
propose EVA-CLIP, a series of models that significantly improve the efficiency
and effectiveness of CLIP training. Our approach incorporates new techniques
for representation learning, optimization, and augmentation, enabling EVA-CLIP
to achieve superior performance compared to previous CLIP models with the same
number of parameters but significantly smaller training costs. Notably, our
largest 5.0B-parameter EVA-02-CLIP-E/14+ with only 9 billion seen samples
achieves 82.0 zero-shot top-1 accuracy on ImageNet-1K val. A smaller
EVA-02-CLIP-L/14+ with only 430 million parameters and 6 billion seen samples
achieves 80.4 zero-shot top-1 accuracy on ImageNet-1K val. To facilitate open
access and open research, we release the complete suite of EVA-CLIP to the
community at https://github.com/baaivision/EVA/tree/master/EVA-CLIP.Comment: To Rei and the moon. Code & Models:
https://github.com/baaivision/EVA/tree/master/EVA-CLI
Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection
Anti-spoofing detection has become a necessity for face recognition systems
due to the security threat posed by spoofing attacks. Despite great success in
traditional attacks, most deep-learning-based methods perform poorly in 3D
masks, which can highly simulate real faces in appearance and structure,
suffering generalizability insufficiency while focusing only on the spatial
domain with single frame input. This has been mitigated by the recent
introduction of a biomedical technology called rPPG (remote
photoplethysmography). However, rPPG-based methods are sensitive to noisy
interference and require at least one second (> 25 frames) of observation time,
which induces high computational overhead. To address these challenges, we
propose a novel 3D mask detection framework, called FASTEN
(Flow-Attention-based Spatio-Temporal aggrEgation Network). We tailor the
network for focusing more on fine-grained details in large movements, which can
eliminate redundant spatio-temporal feature interference and quickly capture
splicing traces of 3D masks in fewer frames. Our proposed network contains
three key modules: 1) a facial optical flow network to obtain non-RGB
inter-frame flow information; 2) flow attention to assign different
significance to each frame; 3) spatio-temporal aggregation to aggregate
high-level spatial features and temporal transition features. Through extensive
experiments, FASTEN only requires five frames of input and outperforms eight
competitors for both intra-dataset and cross-dataset evaluations in terms of
multiple detection metrics. Moreover, FASTEN has been deployed in real-world
mobile devices for practical 3D mask detection.Comment: 13 pages, 5 figures. Accepted to NeurIPS 202
Instance by Instance: An Iterative Framework for Multi-instance 3D Registration
Multi-instance registration is a challenging problem in computer vision and
robotics, where multiple instances of an object need to be registered in a
standard coordinate system. In this work, we propose the first iterative
framework called instance-by-instance (IBI) for multi-instance 3D registration
(MI-3DReg). It successively registers all instances in a given scenario,
starting from the easiest and progressing to more challenging ones. Throughout
the iterative process, outliers are eliminated continuously, leading to an
increasing inlier rate for the remaining and more challenging instances. Under
the IBI framework, we further propose a sparse-to-dense-correspondence-based
multi-instance registration method (IBI-S2DC) to achieve robust MI-3DReg.
Experiments on the synthetic and real datasets have demonstrated the
effectiveness of IBI and suggested the new state-of-the-art performance of
IBI-S2DC, e.g., our MHF1 is 12.02%/12.35% higher than the existing
state-of-the-art method ECC on the synthetic/real datasets.Comment: 14 pages, 12 figures, 10 table
Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance
Active crop sensor-based precision nitrogen (N) management can significantly improve N use efficiency but generally does not increase crop yield. The objective of this research was to develop and evaluate an active canopy sensor-based precision rice management system in terms of grain yield and quality, N use efficiency, and lodging resistance as compared with farmer practice, regional optimum rice management system recommended by the extension service, and a chlorophyll meter-based precision rice management system. Two field experiments were conducted from 2011 to 2013 at Jiansanjiang Experiment Station of China Agricultural University in Heilongjiang, China, involving four rice management systems and two varieties (Kongyu 131 and Longjing 21). The results indicated that the canopy sensor-based precision rice management system significantly increased rice grain yield (by 9.4–13.5%) over the farmer practice while improving N use efficiency, grain quality, and lodging resistance. Compared with the already optimized regional optimum rice management system, in the cool weather year of 2011, the developed system decreased the N rate applied in Kongyu 131 by 12% and improved N use efficiency without inducing yield loss. In the warm weather year of 2013, the canopy sensor-based management system recommended an 8% higher N rate to be applied in Longjing 21 than the regional optimum rice management, which improved rice panicle number per unit area and eventually led to increased grain yield by over 10% and improved N use efficiency. More studies are needed to further test the developed active canopy sensor-based precision rice management system under more diverse on-farm conditions and further improve it using unmanned aerial vehicle or satellite remote sensing technologies for large-scale applications.publishedVersio
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