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Found 17 papers, 6 papers with code
Deconfounded Imitation Learning
Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent.
Modality-Agnostic Topology Aware Localization
This work presents a data-driven approach for the indoor localization of an observer on a 2D topological map of the environment.
Spatial-Temporal Transformer for Dynamic Scene Graph Generation
Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation.
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images
A RANSAC estimator guided by a neural network fits these primitives to 3D features, such as a depth map.
NODIS: Neural Ordinary Differential Scene Understanding
Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image.
Ranked #8 on
Scene Graph Generation
on Visual Genome
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements.
Learning Disentangled Representations via Independent Subspaces
We use face images as our example of choice.
Temporally Consistent Horizon Lines
The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision.
Ranked #1 on
Horizon Line Estimation
on KITTI Horizon
Non-Rigid Structure-From-Motion by Rank-One Basis Shapes
The right singular vectors are affinely back-projected into the 3D space, and the affine back-projections will also be solved as part of the factorisation.
Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis
The word general refers to an approach that recovers the non-rigid affine structure and motion from 2D point correspondences by assuming that (1) the non-rigid shapes are generated by a linear combination of rigid 3D basis shapes, (2) that the non-rigid shapes are affine in nature, i. e., they can be modelled as deviations from the mean, rigid shape, (3) and that the basis shapes are statistically independent.
Object Recognition from very few Training Examples for Enhancing Bicycle Maps
These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples.
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
We present a novel approach for vanishing point detection from uncalibrated monocular images.
Ranked #3 on
Horizon Line Estimation
on York Urban Dataset
A Kinematic Chain Space for Monocular Motion Capture
This paper deals with motion capture of kinematic chains (e. g. human skeletons) from monocular image sequences taken by uncalibrated cameras.
Motion Segmentation via Global and Local Sparse Subspace Optimization
In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model.
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.
On Support Relations and Semantic Scene Graphs
In contrast to previous methods for extracting support relations, the proposed approach generates more accurate results, and does not require a pixel-wise semantic labeling of the scene.
Unbiased Sparse Subspace Clustering By Selective Pursuit
If these unknown subspaces are well-separated this algorithm is guaranteed to succeed.
