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Found 6 papers, 1 papers with code
SyncDreamer for 3D Reconstruction of Endangered Animal Species with NeRF and NeuS
The main aim of this study is to demonstrate how innovative view synthesis and 3D reconstruction techniques can be used to create models of endangered species using monocular RGB images.
Exploring the Feasibility of Generating Realistic 3D Models of Endangered Species Using DreamGaussian: An Analysis of Elevation Angle's Impact on Model Generation
We hope this study to be influential for the usability of AI to preserve endangered animals; while the penultimate aim is to obtain a model that can output biologically consistent 3D models via small samples, the qualitative interpretation of an existing state-of-the-art model such as DreamGaussian will be a step forward in our goal.
Lesion Detection on Leaves using Class Activation Maps
In this study, we propose a method for lesion detection on plant leaves utilizing class activation maps generated by a ResNet-18 classifier.
Improving Perceptual Quality of Adversarial Images Using Perceptual Distance Minimization and Normalized Variance Weighting
We show that these methods could also be used in conjunction to improve the perceptual quality of adversarial examples and demonstrate the quantitative improvements on CIFAR-10 and NIPS2017 Adversarial Learning Challenge datasets.
Training Universal Adversarial Perturbations with Alternating Loss Functions
Despite being very successful, deep learning models were shown to be vulnerable to crafted perturbations.
Imperceptible Adversarial Examples by Spatial Chroma-Shift
Deep Neural Networks have been shown to be vulnerable to various kinds of adversarial perturbations.
