At CEA LIST, we are conducting research in artificial intelligence applied to computer vision for scene understanding.

Our work focuses on the design of new perception models based on machine learning, including deep learning, for visual recognition in images and videos, analysis of interactions, behaviour analysis of individuals, groups and crowds, visual data analysis and characterization, and smart data annotation.

Research topics

Visual recognition

Fast and robust object detection

Lapnet: a single-shot object detector with state-of-the-art performance/inference time trade-off

Fine-grained object recognition

Learning color and edge shared representation from real and synthetic data

Multi-task deep convolutional neural networks for object recognition and semantic segmentation

Deep MANTA (MAny TASk) and Single-Shot Deep MANTA

3D human pose estimation from 2D images

PandaNet: an anchor-based single-shot deep neural network for human detection, 2D and 3D pose estimation

Appearance modeling for person re-identification

End-to-end traning of deep CNN for joint person detection and re-identification

Semantic attribute parsing

Unified framework for semantic attribute and instance segmentation

Behaviour analysis

Real-time Multiple Object Tracking

Track all objects simultaneously in a camera or a camera network by combining object detection and re-id visual appearance features

Detecting Interactions

CALIPSO: interaction detection by associating person and objetcs of a scene with a specific interaction label

Activity recognition

Recognize everyday activities by analyzing people’s movements

Event detection in videos

RIMOC : a feature to discriminate unstructured motions, application to violent event detection

Crowd behaviour analysis

Crowd-11, a dataset of crowd scenes annotated with 11 classes of behaviour, and CrowdCNN a deep convolutional neural network to discriminate them

Beyond fully supervised learning

Learning with few data

Meta-learning: from theory to practice. Learning to learn with few data from multiple elementary tasks, in order to be able to quickly adapt to the target task, with improved generalization capacity.

One-class learning for anomaly detection

Patch Distribution Modeling method (PaDiM), a simple yet effective method to detect and localize anomalies in images.

Self-supervised learning of image and video representations

Self-supervised approach based on Contrastive Predictive Coding (CPC) with fully convolutional architecture

Domain adaptation for person re-identification

An unsupervised domain adaptation framework that allows adaptability to the target domain while ensuring robustness to noisy pseudo-labels

Smart annotation of visual data

Multimodal visual data annotation

Fusion of 2D (RGB images from cameras) and 3D (point clouds from LIDAR) data for the annotation of 3D objects and scenes

Interactive detection and segmentation

Deep Learning algorithms for interactive instance segmentation requiring very little human effort

Automatic label temporal propagation

Automatic propagation of 2D and 3D annotations in time sequences by intelligent interpolation, visual tracking in images and point clouds

PIXANO: an opensource, smart annotation tool for Computer Vision applications

Efficient, large-scale, AI-automated image and video annotation solution offering a wide range of tools integrated into open, modular, reusable, and customizable web-based components.

Active learning for object detection and image segmentation

Relevant image mining and incremental learning of detection and segmentation models by optimizing the ratio between performance and number of labelled image

Reinforcement learning and perception models

Reinforcement learning for autonomous navigation

Learn representation of high-dimensional input data for training agents to navigate by reinforcement learning.

Trustworthy AI

Adversarial attacks and defense in deep metric learning

Self Metric Attack (SMA) and Furthest Negative Attack (FNA), two new adversarial attacks of metric embedding, and a new efficient extension of adversarial training protocol adapted to metric learning as a more robust defense