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Found 8 papers, 4 papers with code
O$n$ Learning Deep O($n$)-Equivariant Hyperspheres
In this paper, we utilize hyperspheres and regular $n$-simplexes and propose an approach to learning deep features equivariant under the transformations of $n$D reflections and rotations, encompassed by the powerful group of O$(n)$.
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis
In our approach, we perform TetraTransform--an equivariant embedding of the 3D input into 4D, constructed from the steerable neurons--and extract deeper O(3)-equivariant features using vector neurons.
Learning What to Learn for Video Object Segmentation
This allows us to achieve a rich internal representation of the target in the current frame, significantly increasing the segmentation accuracy of our approach.
Progress Extrapolating Algorithmic Learning to Arbitrary Sequence Lengths
Recent neural network models for algorithmic tasks have led to significant improvements in extrapolation to sequences much longer than training, but it remains an outstanding problem that the performance still degrades for very long or adversarial sequences.
Learning Fast and Robust Target Models for Video Object Segmentation
The target appearance model consists of a light-weight module, which is learned during the inference stage using fast optimization techniques to predict a coarse but robust target segmentation.
Discriminative Online Learning for Fast Video Object Segmentation
We propose a novel approach, based on a dedicated target appearance model that is exclusively learned online to discriminate between the target and background image regions.
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments.
