Search Results for author:
Found 20 papers, 3 papers with code
CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention
Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices.
LAA-Net: Localized Artifact Attention Network for High-Quality Deepfakes Detection
Second, an Enhanced Feature Pyramid Network (E-FPN) is proposed as a simple and effective mechanism for spreading discriminative low-level features into the final feature output, with the advantage of limiting redundancy.
Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric
Our approach re-frames the neural architecture search problem as finding an architecture with performance similar to that of a reference model for a target hardware, while adhering to a cost constraint for that hardware.
Hardware Aware Neural Architecture Search
Neural Architecture Search
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines
In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions.
Impact of Disentanglement on Pruning Neural Networks
Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency.
DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images
In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis.
SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes
3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas.
Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification
In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed.
Multi-Label Image Classification
Unsupervised Domain Adaptation
CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations
3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry.
TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network
We propose a new neural network architecture for 3D body shape and high-resolution texture completion -- BCom-Net -- that can reconstruct the full geometry from mid-level to high-level partial input scans.
Disentangled Face Identity Representations for joint 3D Face Recognition and Expression Neutralisation
The proposed network consists of three components; (1) a Graph Convolutional Autoencoder (GCA) to encode the 3D faces into latent representations, (2) a Generative Adversarial Network (GAN) that translates the latent representations of expressive faces into those of neutral faces, (3) and an identity recognition sub-network taking advantage of the neutralized latent representations for 3D face recognition.
Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition
This has two disadvantages.
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks.
3DBooSTeR: 3D Body Shape and Texture Recovery
The texture is subsequently obtained by projecting the partial texture onto the template mesh before inpainting the corresponding texture map with a novel approach.
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image.
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories
In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors.
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding
We derived then geometric and computational tools for rate-invariant analysis and adaptive re-sampling of trajectories, grounding on the Riemannian geometry of the underlying manifold.
Deep Covariance Descriptors for Facial Expression Recognition
In this paper, covariance matrices are exploited to encode the deep convolutional neural networks (DCNN) features for facial expression recognition.
A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition
In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation.
Facial Expression Recognition
Facial Expression Recognition (FER)
A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression Recognition
In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation.
Facial Expression Recognition
Facial Expression Recognition (FER)
