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Found 6 papers, 2 papers with code
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking
Summarizing, this paper firstly mathematically defines the strategies used to make Vision Transformer efficient, describes and discusses state-of-the-art methodologies, and analyzes their performances over different application scenarios.
DANAE: a denoising autoencoder for underwater attitude estimation
One of the main issues for underwater robots navigation is their accurate positioning, which heavily depends on the orientation estimation phase.
Hallucinating Agnostic Images to Generalize Across Domains
The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems.
From source to target and back: symmetric bi-directional adaptive GAN
The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem.
Ranked #13 on
Domain Adaptation
on SVHN-to-MNIST
A deep representation for depth images from synthetic data
We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets.
Learning the Roots of Visual Domain Shift
In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set.
