Search Results for author:
Found 31 papers, 15 papers with code
Leveraging Acoustic Images for Effective Self-Supervised Audio Representation Learning
However, since 2D planar arrays are cumbersome and not as widespread as ordinary microphones, we propose that the richer information content of acoustic images can be distilled, through a self-supervised learning scheme, into more powerful audio and visual feature representations.
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation
We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels.
Self-improving object detection via disagreement reconciliation
Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data.
Anticipating Next Active Objects for Egocentric Videos
This paper addresses the problem of anticipating the next-active-object location in the future, for a given egocentric video clip where the contact might happen, before any action takes place.
Look around and learn: self-improving object detection by exploration
Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data.
3DSGrasp: 3D Shape-Completion for Robotic Grasp
However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses.
Lifting Monocular Events to 3D Human Poses
Here we propose the first learning-based method for 3D human pose from a single stream of events.
Ranked #2 on
3D Human Pose Estimation
on DHP19
Compact CNN Structure Learning by Knowledge Distillation
The concept of compressing deep Convolutional Neural Networks (CNNs) is essential to use limited computation, power, and memory resources on embedded devices.
Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation
On the contrary, a pre-trained source model is always considered to be available, even though performing poorly on target due to the well-known domain shift problem.
Single Image Human Proxemics Estimation for Visual Social Distancing
In this work, we address the problem of estimating the so-called "Social Distancing" given a single uncalibrated image in unconstrained scenarios.
Cross-modal Speaker Verification and Recognition: A Multilingual Perspective
Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition.
Complex-Object Visual Inspection via Multiple Lighting Configurations
The design of an automatic visual inspection system is usually performed in two stages.
Intra-Camera Supervised Person Re-Identification
Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data.
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
We exploit this finding in an iterative procedure where a generative model and a classifier are jointly trained: in turn, the generator allows to sample cleaner data from the target distribution, and the classifier allows to associate better labels to target samples, progressively refining target pseudo-labels.
DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible missing modalities at test time.
Unsupervised Domain-Adaptive Person Re-identification Based on Attributes
Training person re-identification (ReID) algorithms under the supervision of such attributes have proven to be effective in extracting local features which are important for ReID.
Domain Adaptation
Domain Adaptive Person Re-Identification
+2
Audio-Visual Model Distillation Using Acoustic Images
In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and acoustic images, a novel audio data modality.
Cross-modal Learning by Hallucinating Missing Modalities in RGB-D Vision
We report state-of-the-art or comparable results on video action recognition on the largest multimodal dataset available for this task, the NTU RGB+D, as well as on the UWA3DII and Northwestern-UCLA.
Learning with privileged information via adversarial discriminative modality distillation
This raises the challenge of how to extract information from multimodal data in the training stage, in a form that can be exploited at test time, considering limitations such as noisy or missing modalities.
Modality Distillation with Multiple Stream Networks for Action Recognition
Particularly, we consider the case of learning representations from depth and RGB videos, while relying on RGB data only at test time.
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout.
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages on our finding that entropy minimization is induced by the optimal alignment of second order statistics between source and target domains.
Scalable and Compact 3D Action Recognition with Approximated RBF Kernel Machines
Despite the recent deep learning (DL) revolution, kernel machines still remain powerful methods for action recognition.
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to train powerful classifiers for the target samples.
Dropout as a Low-Rank Regularizer for Matrix Factorization
Regularization for matrix factorization (MF) and approximation problems has been carried out in many different ways.
A Compact Kernel Approximation for 3D Action Recognition
In this work we reduce such complexity to be linear by proposing a novel and explicit feature map to approximate the kernel function.
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors.
Correlation Alignment by Riemannian Metric for Domain Adaptation
Domain adaptation techniques address the problem of reducing the sensitivity of machine learning methods to the so-called domain shift, namely the difference between source (training) and target (test) data distributions.
Curriculum Dropout
This induces an adaptive regularization scheme that smoothly increases the difficulty of the optimization problem.
Left/Right Hand Segmentation in Egocentric Videos
Due to their favorable location, wearable cameras frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications.
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective.
