Zobrazeno 1 - 10
of 370
pro vyhledávání: '"Monga, Vishal"'
In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling-based deep network design since ma
Externí odkaz:
http://arxiv.org/abs/2402.12872
Active Learning (AL) for semantic segmentation is challenging due to heavy class imbalance and different ways of defining "sample" (pixels, areas, etc.), leaving the interpretation of the data distribution ambiguous. We propose "Maturity-Aware Distri
Externí odkaz:
http://arxiv.org/abs/2308.14904
Synthetic aperture sonar (SAS) systems produce high-resolution images of the seabed environment. Moreover, deep learning has demonstrated superior ability in finding robust features for automating imagery analysis. However, the success of deep learni
Externí odkaz:
http://arxiv.org/abs/2203.15082
Detecting and evaluating surface coating defects is important for marine vessel maintenance. Currently, the assessment is carried out manually by qualified inspectors using international standards and their own experience. Automating the processes is
Externí odkaz:
http://arxiv.org/abs/2203.09580
Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and is often formulated as a multi-label classification problem. Yet signi
Externí odkaz:
http://arxiv.org/abs/2203.03079
CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles
Self-driving vehicles are the future of transportation. With current advancements in this field, the world is getting closer to safe roads with almost zero probability of having accidents and eliminating human errors. However, there is still plenty o
Externí odkaz:
http://arxiv.org/abs/2111.08243
In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling, an important technique that bridges between traditional iterative algorithms and modern deep learning techniques. To provide context, we start by
Externí odkaz:
http://arxiv.org/abs/2108.06637
Deep learning has not been routinely employed for semantic segmentation of seabed environment for synthetic aperture sonar (SAS) imagery due to the implicit need of abundant training data such methods necessitate. Abundant training data, specifically
Externí odkaz:
http://arxiv.org/abs/2107.14563
Image relighting has emerged as a problem of significant research interest inspired by augmented reality applications. Physics-based traditional methods, as well as black box deep learning models, have been developed. The existing deep networks have
Externí odkaz:
http://arxiv.org/abs/2105.02209
Autor:
Yazdani, Amirsaeed, Agrawal, Sumit, Johnstonbaugh, Kerrick, Kothapalli, Sri-Rajasekhar, Monga, Vishal
A significant research problem of recent interest is the localization of targets like vessels, surgical needles, and tumors in photoacoustic (PA) images. To achieve accurate localization, a high photoacoustic signal-to-noise ratio (SNR) is required.
Externí odkaz:
http://arxiv.org/abs/2104.14713