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Found 17 papers, 5 papers with code
Knowledge Combination to Learn Rotated Detection Without Rotated Annotation
Annotating rotated bounding boxes is such a laborious process that they are not provided in many detection datasets where axis-aligned annotations are used instead.
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups
Each learning group consists of a teacher network, a student network and a novel filter module.
Retrieval Augmented Classification for Long-Tail Visual Recognition
We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module.
Ranked #6 on
Long-tail Learning
on iNaturalist 2018
NeuRoRA: Neural Robust Rotation Averaging
Although the idea of replacing robust optimization methods by a graph-based network is demonstrated only for multiple rotation averaging, it could easily be extended to other graph-based geometric problems, for example, pose-graph optimization.
SG-VAE: Scene Grammar Variational Autoencoder to generate new indoor scenes
Our method learns the co-occurrences, and appearance parameters such as shape and pose, for different objects categories through a grammar-based auto-encoder, resulting in a compact and accurate representation for scene layouts.
Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints
Planar markers are useful in robotics and computer vision for mapping and localisation.
Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions
In our experiments we demonstrate that a CNN trained by minimizing the proposed loss is able to predict semantic categories for visible and occluded object parts without requiring to increase the network size (compared to a standard segmentation task).
Morphological Networks for Image De-raining
Mathematical morphological methods have successfully been applied to filter out (emphasize or remove) different structures of an image.
Weakly supervised learning of indoor geometry by dual warping
A major element of depth perception and 3D understanding is the ability to predict the 3D layout of a scene and its contained objects for a novel pose.
Maximum Consensus Parameter Estimation by Reweighted $\ell_1$ Methods
Robust parameter estimation in computer vision is frequently accomplished by solving the maximum consensus (MaxCon) problem.
Learning monocular visual odometry with dense 3D mapping from dense 3D flow
Dense 2D flow and a depth image are generated from monocular images by sub-networks, which are then used by a 3D flow associated layer in the L-VO network to generate dense 3D flow.
SPP-Net: Deep Absolute Pose Regression with Synthetic Views
In this work we design a deep neural network architecture based on sparse feature descriptors to estimate the absolute pose of an image.
Minimal Solvers for Monocular Rolling Shutter Compensation under Ackermann Motion
Modern automotive vehicles are often equipped with a budget commercial rolling shutter camera.
Rolling Shutter Correction in Manhattan World
A vast majority of consumer cameras operate the rolling shutter mechanism, which often produces distorted images due to inter-row delay while capturing an image.
Dense RGB-D semantic mapping with Pixel-Voxel neural network
For intelligent robotics applications, extending 3D mapping to 3D semantic mapping enables robots to, not only localize themselves with respect to the scene's geometrical features but also simultaneously understand the higher level meaning of the scene contexts.
Clustering with Hypergraphs: The Case for Large Hyperedges
The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision.
Efficient Globally Optimal Consensus Maximisation With Tree Search
We aim to change this state of affairs by proposing a very efficient algorithm for global maximisation of consensus.
