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Found 7 papers, 3 papers with code
ViewSynth: Learning Local Features from Depth using View Synthesis
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.
Towards Single Stage Weakly Supervised Semantic Segmentation
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
Weakly-Supervised Semantic Segmentation
Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks
By extension, effective representation of material and texture can describe other semantic classes strongly associated with said material and texture.
Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors
Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers.
H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement
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.
AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk
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.
Self-Supervised Object Detection from Egocentric Videos
Understanding the visual world from the perspective of humans (egocentric) has been a long-standing challenge in computer vision.
