Search Results for author:
Found 10 papers, 2 papers with code
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.
UNTAG: LEARNING GENERIC FEATURES FOR UNSUPERVISED TYPE-AGNOSTIC DEEPFAKE DETECTION
This paper introduces a novel framework for unsupervised type-agnostic deepfake detection called UNTAG.
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.
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
Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains
This paper proposes an adaptive graph-based approach for multi-label image classification.
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods
Notwithstanding the relevance of this topic in numerous application fields, a complete and extensive evaluation of recent state-of-the-art techniques is still missing.
Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN)
In this paper, a novel graph-based approach for multi-label image classification called Multi-Label Adaptive Graph Convolutional Network (ML-AGCN) is introduced.
Ranked #1 on
Multi-Label Image Classification
on MSCOCO
(mean average precision metric)
LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network
Being capable of estimating the pose of uncooperative objects in space has been proposed as a key asset for enabling safe close-proximity operations such as space rendezvous, in-orbit servicing and active debris removal.
Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognition
This paper extends the Spatial-Temporal Graph Convolutional Network (ST-GCN) for skeleton-based action recognition by introducing two novel modules, namely, the Graph Vertex Feature Encoder (GVFE) and the Dilated Hierarchical Temporal Convolutional Network (DH-TCN).
Ranked #13 on
Action Recognition
on NTU RGB+D 120
Localized Trajectories for 2D and 3D Action Recognition
The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion.
