References of "Aouada, Djamila 50000437"
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See detailIML-GCN: Improved Multi-Label Graph Convolutional Network for Efficient yet Precise Image Classification
Singh, Inder Pal UL; Oyedotun, Oyebade UL; Ghorbel, Enjie UL et al

in AAAI-22 Workshop Program-Deep Learning on Graphs: Methods and Applications (2022, February)

In this paper, we propose the Improved Multi-Label Graph Convolutional Network (IML-GCN) as a precise and efficient framework for multi-label image classification. Although previous approaches have shown ... [more ▼]

In this paper, we propose the Improved Multi-Label Graph Convolutional Network (IML-GCN) as a precise and efficient framework for multi-label image classification. Although previous approaches have shown great performance, they usually make use of very large architectures. To handle this, we propose to combine the small version of a newly introduced network called TResNet with an extended version of Multi-label Graph Convolution Networks (ML-GCN); therefore ensuring the learning of label correlation while reducing the size of the overall network. The proposed approach considers a novel image feature embedding instead of using word embeddings. In fact, the latter are learned from words and not images making them inadequate for the task of multi-label image classification. Experimental results show that our framework competes with the state-of-the-art on two multi-label image benchmarks in terms of both precision and memory requirements. [less ▲]

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See detailWhy is Everyone Training Very Deep Neural Network with Skip Connections?
Oyedotun, Oyebade UL; Al Ismaeil, Kassem; Aouada, Djamila UL

in IEEE Transactions on Neural Networks and Learning Systems (2021)

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See detailLeveraging High-Frequency Components for Deepfake Detection
Mejri, Nesryne UL; Papadopoulos, Konstantinos UL; Aouada, Djamila UL

in IEEE Workshop on Multimedia Signal Processing (2021)

In the past years, RGB-based deepfake detection has shown notable progress thanks to the development of effective deep neural networks. However, the performance of deepfake detectors remains primarily ... [more ▼]

In the past years, RGB-based deepfake detection has shown notable progress thanks to the development of effective deep neural networks. However, the performance of deepfake detectors remains primarily dependent on the quality of the forged content and the level of artifacts introduced by the forgery method. To detect these artifacts, it is often necessary to separate and analyze the frequency components of an image. In this context, we propose to utilize the high-frequency components of color images by introducing an end-to-end trainable module that (a) extracts features from high-frequency components and (b) fuses them with the features of the RGB input. The module not only exploits the high-frequency anomalies present in manipulated images but also can be used with most RGB-based deepfake detectors. Experimental results show that the proposed approach boosts the performance of state-of-the-art networks, such as XceptionNet and EfficientNet, on a challenging deepfake dataset. [less ▲]

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See detailLeveraging Temporal Information for 3D Trajectory Estimation of Space Objects
Mohamed Ali, Mohamed Adel UL; Ortiz Del Castillo, Miguel UL; Al Ismaeil, Kassem UL et al

in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (2021, October)

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See detailLSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network
Garcia Sanchez, Albert UL; Mohamed Ali, Mohamed Adel UL; Gaudilliere, Vincent UL et al

in Proceedings of Conference on Computer Vision and Pattern Recognition Workshops (2021, June)

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 ... [more ▼]

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. Usual approaches for pose estimation involve classical computer vision-based solutions or the application of Deep Learning (DL) techniques. This work explores a novel DL-based methodology, using Convolutional Neural Networks (CNNs), for estimating the pose of uncooperative spacecrafts. Contrary to other approaches, the proposed CNN directly regresses poses without needing any prior 3D information. Moreover, bounding boxes of the spacecraft in the image are predicted in a simple, yet efficient manner. The performed experiments show how this work competes with the state-of-the-art in uncooperative spacecraft pose estimation, including works which require 3D information as well as works which predict bounding boxes through sophisticated CNNs. [less ▲]

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See detailDisentangled Face Identity Representations for joint 3D Face Recognition and Expression Neutralisation
Kacem, Anis UL; cherenkova, kseniya; Aouada, Djamila UL

E-print/Working paper (2021)

In this paper, we propose a new deep learning-based approach for disentangling face identity representations from expressive 3D faces. Given a 3D face, our approach not only extracts a disentangled ... [more ▼]

In this paper, we propose a new deep learning-based approach for disentangling face identity representations from expressive 3D faces. Given a 3D face, our approach not only extracts a disentangled identity representation but also generates a realistic 3D face with a neutral expression while predicting its identity. 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. The whole network is trained in an end-to-end manner. Experiments are conducted on three publicly available datasets showing the effectiveness of the proposed approach. [less ▲]

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See detailFace-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition
Papadopoulos, Konstantinos; Kacem, Anis UL; Shabayek, Abdelrahman et al

E-print/Working paper (2021)

Face identification/recognition has significantly advanced over the past years. However, most of the proposed approaches rely on static RGB frames and on neutral facial expressions. This has two ... [more ▼]

Face identification/recognition has significantly advanced over the past years. However, most of the proposed approaches rely on static RGB frames and on neutral facial expressions. This has two disadvantages. First, important facial shape cues are ignored. Second, facial deformations due to expressions can have an impact on the performance of such a method. In this paper, we propose a novel framework for dynamic 3D face identification/recognition based on facial keypoints. Each dynamic sequence of facial expressions is represented as a spatio-temporal graph, which is constructed using 3D facial landmarks. Each graph node contains local shape and texture features that are extracted from its neighborhood. For the classification/identification of faces, a Spatio-temporal Graph Convolutional Network (ST-GCN) is used. Finally, we evaluate our approach on a challenging dynamic 3D facial expression dataset. [less ▲]

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See detailVertex Feature Encoding and Hierarchical Temporal Modeling in a Spatio-Temporal Graph Convolutional Network for Action Recognition
Papadopoulos, Konstantinos UL; Ghorbel, Enjie UL; Aouada, Djamila UL et al

in International Conference on Pattern Recognition, Milan 10-15 January 2021 (2021)

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See detailSPACECRAFT RECOGNITION LEVERAGING KNOWLEDGE OF SPACE ENVIRONMENT: SIMULATOR, DATASET, COMPETITION DESIGN, AND ANALYSIS
Mohamed Ali, Mohamed Adel UL; Gaudilliere, Vincent UL; Ghorbel, Enjie UL et al

in 2021 IEEE International Conference on Image Processing (ICIP) (2021)

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See detailDetection & Identification of On-Orbit Objects Using Machine Learning
Perez, Marcos; Mohamed Ali, Mohamed Adel UL; Garcia Sanchez, Albert UL et al

in European Conference on Space Debris (2021), 8(1),

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See detailExplaining Defect Detection with Saliency Maps
Lorentz, Joe UL; Hartmann, Thomas; Moawad, Assaad et al

E-print/Working paper (2021)

The rising quality and throughput demands of the manufacturing domain require flexible, accurate and explainable computer-vision solutions for defect detection. Deep Neural Networks (DNNs) reach state-of ... [more ▼]

The rising quality and throughput demands of the manufacturing domain require flexible, accurate and explainable computer-vision solutions for defect detection. Deep Neural Networks (DNNs) reach state-of-the-art performance on various computer-vision tasks but wide-spread application in the industrial domain is blocked by the lacking explainability of DNN decisions. A promising, human-readable solution is given by saliency maps, heatmaps highlighting the image areas that influence the classifier’s decision. This work evaluates a selection of saliency methods in the area of industrial quality assurance. To this end we propose the distance pointing game, a new metric to quantify the meaningfulness of saliency maps for defect detection. We provide steps to prepare a publicly available dataset on defective steel plates for the proposed metric. Additionally, the computational complexity is investigated to determine which methods could be integrated on industrial edge devices. Our results show that DeepLift, GradCAM and GradCAM++ outperform the alternatives while the computational cost is feasible for real time applications even on edge devices. This indicates that the respective methods could be used as an additional, autonomous post-classification step to explain decisions taken by intelligent quality assurance systems. [less ▲]

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See detail3D SPARSE DEFORMATION SIGNATURE FOR DYNAMIC FACE RECOGNITION
Shabayek, Abd El Rahman UL; Aouada, Djamila UL; Cherenkova, Kseniya UL et al

in 27th IEEE International Conference on Image Processing (ICIP 2020), Abu Dhabi 25-28 October 2020 (2020, October)

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