Search Results for author:
Found 16 papers, 3 papers with code
On the De-duplication of LAION-2B
Generative models, such as DALL-E, Midjourney, and Stable Diffusion, have societal implications that extend beyond the field of computer science.
Empowering the trustworthiness of ML-based critical systems through engineering activities
This paper reviews the entire engineering process of trustworthy Machine Learning (ML) algorithms designed to equip critical systems with advanced analytics and decision functions.
Training face verification models from generated face identity data
We find that the addition of a small amount of private data greatly improves the performance of our model, which highlights the limitations of using synthetic data to train machine learning models.
This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces
Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers.
Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
We address the problem of 3D object detection from 2D monocular images in autonomous driving scenarios.
3D Object Detection
3D Object Detection From Monocular Images
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Detecting Overfitting of Deep Generative Networks via Latent Recovery
Using this methodology, this paper shows that overfitting is not detectable in the pure GAN models proposed in the literature, in contrast with those using hybrid adversarial losses, which are amongst the most widely applied generative methods.
Unsupervised part learning for visual recognition
This strategy opens the door to the use of PBM in new applications for which the notion of image categories is irrelevant, such as instance-based image retrieval, for example.
Automatic discovery of discriminative parts as a quadratic assignment problem
Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built.
Deep fusion of visual signatures for client-server facial analysis
The challenge addressed in this paper is to design a common universal representation such that a single merged signature is transmitted to the server, whatever be the type and number of features computed by the client, ensuring nonetheless an optimal performance.
CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval
The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.
Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns
We propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features.
Expanded Parts Model for Semantic Description of Humans in Still Images
We validate our method on three recent challenging datasets of human attributes and actions.
Hybrid multi-layer Deep CNN/Aggregator feature for image classification
A second variant of our approach that includes the fully connected DCNN layers significantly outperforms Fisher vector schemes and performs comparably to DCNN approaches adapted to Pascal VOC 2007, yet at only a small fraction of the training and testing cost.
Vehicle Detection in Aerial Imagery (VEDAI) : a benchmark
VEDAI is a dataset for Vehicle Detection in Aerial Imagery, provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments.
Histograms of Pattern Sets for Image Classification and Object Recognition
This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features.
Expanded Parts Model for Human Attribute and Action Recognition in Still Images
We propose a new model for recognizing human attributes (e. g. wearing a suit, sitting, short hair) and actions (e. g. running, riding a horse) in still images.
