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Found 20 papers, 9 papers with code
Improving the matching of deformable objects by learning to detect keypoints
We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence.
Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval.
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images
We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence.
Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach
Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime.
Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions
In this paper, we propose a synthetic-aware adverse weather robust algorithm for video stabilization that does not require real data and can be trained only on synthetic data.
Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
The growth of videos in our digital age and the users' limited time raise the demand for processing untrimmed videos to produce shorter versions conveying the same information.
Learning Geodesic-Aware Local Features from RGB-D Images
Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfaces.
Extracting Deformation-Aware Local Features by Learning to Deform
Despite the advances in extracting local features achieved by handcrafted and learning-based descriptors, they are still limited by the lack of invariance to non-rigid transformations.
Creating and Reenacting Controllable 3D Humans with Differentiable Rendering
It also infers texture appearance with a convolutional network in the texture domain, which is trained in an adversarial regime to reconstruct human texture from rendered images of actors in different poses.
A Shape-Aware Retargeting Approach to Transfer Human Motion and Appearance in Monocular Videos
Transferring human motion and appearance between videos of human actors remains one of the key challenges in Computer Vision.
Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio
Our method uses an adversarial learning scheme conditioned on the input music audios to create natural motions preserving the key movements of different music styles.
Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues
The formulation is designed to identify and to disregard dynamic objects in order to obtain a medium-term invariant map representation.
Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints
Differently from recent appearance transferring methods, our approach takes into account body shape, appearance, and motion constraints.
Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network
In this work, we present a new approach to automatically creating personalized fast-forward videos for FPVs.
GEOBIT: A Geodesic-Based Binary Descriptor Invariant to Non-Rigid Deformations for RGB-D Images
At the core of most three-dimensional alignment and tracking tasks resides the critical problem of point correspondence.
A Two-Step Learning Method For Detecting Landmarks on Faces From Different Domains
The detection of fiducial points on faces has significantly been favored by the rapid progress in the field of machine learning, in particular in the convolution networks.
Fast forwarding Egocentric Videos by Listening and Watching
The remarkable technological advance in well-equipped wearable devices is pushing an increasing production of long first-person videos.
A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos
Thanks to the advances in the technology of low-cost digital cameras and the popularity of the self-recording culture, the amount of visual data on the Internet is going to the opposite side of the available time and patience of the users.
A Robust Indoor Scene Recognition Method based on Sparse Representation
In this paper, we present a robust method for scene recognition, which leverages Convolutional Neural Networks (CNNs) features and Sparse Coding setting by creating a new representation of indoor scenes.
Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition
We then use it to assign the latent values to the label values.
