Search Results for author:
Found 65 papers, 11 papers with code
HARMamba: Efficient Wearable Sensor Human Activity Recognition Based on Bidirectional Selective SSM
Recently, an efficient hardware-aware state space model (SSM) called Mamba has emerged as a promising alternative.
ChebMixer: Efficient Graph Representation Learning with MLP Mixer
In this paper, we present a novel architecture named ChebMixer, a newly graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens.
MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer
Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features.
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer
Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data.
ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications.
GAPS: Geometry-Aware, Physics-Based, Self-Supervised Neural Garment Draping
Recent neural, physics-based modeling of garment deformations allows faster and visually aesthetic results as opposed to the existing methods.
IP-UNet: Intensity Projection UNet Architecture for 3D Medical Volume Segmentation
IP-UNet is a UNet-based model that performs multi-class segmentation on Intensity Projection (IP) of 3D volumetric data instead of the memory-consuming 3D volumes.
IoT trust and reputation: a survey and taxonomy
In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems.
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation
Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years.
Look Beyond Bias with Entropic Adversarial Data Augmentation
Deep neural networks do not discriminate between spurious and causal patterns, and will only learn the most predictive ones while ignoring the others.
Learning Less Generalizable Patterns with an Asymmetrically Trained Double Classifier for Better Test-Time Adaptation
Indeed, test-time adaptation methods usually have to rely on a limited representation because of the shortcut learning phenomenon: only a subset of the available predictive patterns is learned with standard training.
Attention Regularized Laplace Graph for Domain Adaptation
In this paper, starting from our previous DGA-DA, we propose a novel DA method, namely Attention Regularized Laplace Graph-based Domain Adaptation (ARG-DA), to remedy the aforementioned issues.
Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing
The Metaverse is a virtual environment where users are represented by avatars to navigate a virtual world, which has strong links with the physical one.
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Precise representations of 3D faces are beneficial to various computer vision and graphics applications.
Ranked #2 on
Face Alignment
on FaceScape
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition
Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples.
Non-Deterministic Face Mask Removal Based On 3D Priors
This paper presents a novel image inpainting framework for face mask removal.
Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review
Death by suicide is the seventh leading death cause worldwide.
FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction
Occlusions often occur in face images in the wild, troubling face-related tasks such as landmark detection, 3D reconstruction, and face recognition.
Segmentation-Reconstruction-Guided Facial Image De-occlusion
The proposed model consists of a 3D face reconstruction module, a face segmentation module, and an image generation module.
When Neural Networks Using Different Sensors Create Similar Features
Multimodal problems are omnipresent in the real world: autonomous driving, robotic grasping, scene understanding, etc... We draw from the well-developed analysis of similarity to provide an example of a problem where neural networks are trained from different sensors, and where the features extracted from these sensors still carry similar information.
The Devil Is in the Details: An Efficient Convolutional Neural Network for Transport Mode Detection
Transport mode detection is a classification problem aiming to design an algorithm that can infer the transport mode of a user given multimodal signals (GPS and/or inertial sensors).
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition
The experiment results show that the resampling augmentation method outperforms all state-of-the-art methods under a small amount of labeled data, on SimCLRHAR and MoCoHAR, with mean F1-score as the evaluation metric.
Multi-Goal Reinforcement Learning environments for simulated Franka Emika Panda robot
This technical report presents panda-gym, a set Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym.
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization
Traditional deep learning algorithms often fail to generalize when they are tested outside of the domain of the training data.
Pixel Sampling for Style Preserving Face Pose Editing
The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc.
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction
Missing textures in the incomplete UV map are further full-filled by the UV generator.
Data Fusion for Deep Learning on Transport Mode Detection: A Case Study
In Transport Mode Detection, a great diversity of methodologies exist according to the choice made on sensors, preprocessing, model used, etc.
Connecting Images through Time and Sources: Introducing Low-data, Heterogeneous Instance Retrieval
Pick a training dataset, pick a backbone network for feature extraction, and voil\`a ; this usually works for a variety of use cases.
A Survey of Hybrid Human-Artificial Intelligence for Social Computing
In particular with the boom of artificial intelligence (AI), social computing is significantly influenced by AI.
Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning
Advances in high resolution remote sensing image analysis are currently hampered by the difficulty of gathering enough annotated data for training deep learning methods, giving rise to a variety of small datasets and associated dataset-specific methods.
IoT-Enabled Social Relationships Meet Artificial Social Intelligence
With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade.
Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation
Domain adaptation (DA) aims to enable a learning model trained from a source domain to generalize well on a target domain, despite the mismatch of data distributions between the two domains.
A 3D GAN for Improved Large-pose Facial Recognition
Facial recognition using deep convolutional neural networks relies on the availability of large datasets of face images.
Robustness of Facial Recognition to GAN-based Face-morphing Attacks
Face-morphing attacks have been a cause for concern for a number of years.
An Assessment of GANs for Identity-related Applications
Generative Adversarial Networks (GANs) are now capable of producing synthetic face images of exceptionally high visual quality.
One-shot Distributed Algorithm for Generalized Eigenvalue Problem
Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy.
Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction
In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects.
Breaking Batch Normalization for better explainability of Deep Neural Networks through Layer-wise Relevance Propagation
The relevance spreads backward from the last to the first layer of the Deep Neural Network.
Scoring Graspability based on Grasp Regression for Better Grasp Prediction
Therefore, in this paper, we extend a state-of-the-art neural network with a scorer that evaluates the graspability of a given position, and introduce a novel loss function which correlates regression of grasp parameters with graspability score.
Challenging deep image descriptors for retrieval in heterogeneous iconographic collections
This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date and multi-view Permission to make digital
Toward a Procedural Fruit Tree Rendering Framework for Image Analysis
We propose a procedural fruit tree rendering framework, based on Blender and Python scripts allowing to generate quickly labeled dataset (i. e. including ground truth semantic segmentation).
Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image
We thus also introduce a synthetic dataset of dense homogeneous object layouts, namely Mikado, which extensibly contains more instances and inter-instance occlusions per image than these public datasets.
Taking Control of Intra-class Variation in Conditional GANs Under Weak Supervision
For example, given only labels of ambient / non-ambient lighting, our method is able to learn multivariate lighting models disentangled from other factors such as the identity and pose.
Image-based Natural Language Understanding Using 2D Convolutional Neural Networks
We propose a new approach to natural language understanding in which we consider the input text as an image and apply 2D Convolutional Neural Networks to learn the local and global semantics of the sentences from the variations ofthe visual patterns of words.
Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning
In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i. e. learning from scratch all the time).
Jacquard: A Large Scale Dataset for Robotic Grasp Detection
Jacquard is built on a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D images and annotations of successful grasping positions based on grasp attempts performed in a simulated environment.
Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition
In particular, each textured 3D face scan is firstly represented as a 2D texture map and a depth map with one-to-one dense correspondence.
A Deep Learning Approach for Privacy Preservation in Assisted Living
In the era of Internet of Things (IoT) technologies the potential for privacy invasion is becoming a major concern especially in regards to healthcare data and Ambient Assisted Living (AAL) environments.
Discriminative Label Consistent Domain Adaptation
Domain adaptation (DA) is transfer learning which aims to learn an effective predictor on target data from source data despite data distribution mismatch between source and target.
Brenier approach for optimal transportation between a quasi-discrete measure and a discrete measure
Correctly estimating the discrepancy between two data distributions has always been an important task in Machine Learning.
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations.
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
We thus propose a step towards a practical interactive application for generating an object-oriented robotic grasp, requiring as inputs only one depth map of the scene and one user click on the next object to extract.
Discriminative and Geometry Aware Unsupervised Domain Adaptation
Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions.
Improving Shadow Suppression for Illumination Robust Face Recognition
2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world.
Densely tracking sequences of 3D face scans
3D face dense tracking aims to find dense inter-frame correspondences in a sequence of 3D face scans and constitutes a powerful tool for many face analysis tasks, e. g., 3D dynamic facial expression analysis.
Optimal Transport for Deep Joint Transfer Learning
By adding an Optimal Transport loss (OT loss) between source and target classifier predictions as a constraint on the source classifier, the proposed Joint Transfer Learning Network (JTLN) can effectively learn useful knowledge for target classification from source data.
Improving Heterogeneous Face Recognition with Conditional Adversarial Networks
Heterogeneous face recognition between color image and depth image is a much desired capacity for real world applications where shape information is looked upon as merely involved in gallery.
von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification
Results show the effectiveness and excellent generalization ability of the proposed approach as it achieves state-of-the-art results on the LFW, YouTube faces and CACD datasets and competitive results on the IJB-A dataset.
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation
Domain adaptation (DA) is transfer learning which aims to leverage labeled data in a related source domain to achieve informed knowledge transfer and help the classification of unlabeled data in a target domain.
Close Yet Distinctive Domain Adaptation
Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions.
DeepVisage: Making face recognition simple yet with powerful generalization skills
Although CNNs are mostly trained by optimizing the softmax loss, the recent trend shows an improvement of accuracy with different strategies, such as task-specific CNN learning with different loss functions, fine-tuning on target dataset, metric learning and concatenating features from multiple CNNs.
Ranked #6 on
Age-Invariant Face Recognition
on CACDVS
Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer
This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations.
Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition
In this paper, we present a novel approach to automatic 3D Facial Expression Recognition (FER) based on deep representation of facial 3D geometric and 2D photometric attributes.
3D Facial Expression Recognition
Facial Expression Recognition
Automatic video scene segmentation based on spatial-temporal clues and rhythm
With ever increasing computing power and data storage capacity, the potential for large digital video libraries is growing rapidly. However, the massive use of video for the moment is limited by its opaque characteristics.
3D-aided Face Recognition Robust to Expression and Pose Variations
Specifically, given a probe with expression, a novel view of the face is generated where the pose is rectified and the expression neutralized.
