Search Results for author: Peri Akiva

Found 6 papers, 3 papers with code

Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks

1 code implementation3 Dec 2021 Peri Akiva, Matthew Purri, Matthew Leotta

By extension, effective representation of material and texture can describe other semantic classes strongly associated with said material and texture.

Change Detection Representation Learning +3

Towards Single Stage Weakly Supervised Semantic Segmentation

1 code implementation18 Jun 2021 Peri Akiva, Kristin Dana

The costly process of obtaining semantic segmentation labels has driven research towards weakly supervised semantic segmentation (WSSS) methods, using only image-level, point, or box labels.

Weakly-Supervised Semantic Segmentation

AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk

no code implementations8 Nov 2020 Peri Akiva, Benjamin Planche, Aditi Roy, Kristin Dana, Peter Oudemans, Michael Mars

Toward this goal, we propose two main deep learning-based modules for: 1) cranberry fruit segmentation to delineate the exact fruit regions in the cranberry field image that are exposed to sun, 2) prediction of cloud coverage conditions and sun irradiance to estimate the inner temperature of exposed cranberries.

H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement

no code implementations11 Oct 2020 Peri Akiva, Matthew Purri, Kristin Dana, Beth Tellman, Tyler Anderson

We demonstrate that H2O-Net outperforms the state-of-the-art semantic segmentation methods on satellite imagery by 10% and 12% pixel accuracy and mIoU respectively for the task of flood segmentation.

Domain Adaptation Semantic Segmentation

Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors

no code implementations18 Apr 2020 Peri Akiva, Kristin Dana, Peter Oudemans, Michael Mars

Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers.

ViewSynth: Learning Local Features from Depth using View Synthesis

1 code implementation22 Nov 2019 Jisan Mahmud, Rajat Vikram Singh, Peri Akiva, Spondon Kundu, Kuan-Chuan Peng, Jan-Michael Frahm

By learning view synthesis, we explicitly encourage the feature extractor to encode information about not only the visible, but also the occluded parts of the scene.

Camera Localization Keypoint Detection

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