NoPain: No-box Point Cloud Attack via Optimal Transport Singular Boundary will be presented at CVPR 2025, Nasheville, June 2025

Adversarial attacks exploit the vulnerability of deep models against adversarial samples. Existing point cloud attackers are tailored to specific models, iteratively optimizing perturbations based on gradients in either a white-box or black-box setting. Despite their promising attack performance, they often struggle to produce transferable adversarial samples due to overfitting the specific parameters of surrogate models. To overcome this issue, we shift our focus to the data distribution itself and introduce a novel approach named NoPain, which employs optimal transport (OT) to identify the inherent singular boundaries of the data manifold for cross-network point cloud attacks. Specifically, we first calculate the OT mapping from noise to the target feature space, then identify singular boundaries by locating non-differentiable positions. Finally, we sample along singular boundaries to generate adversarial point clouds. Once the singular boundaries are determined, NoPain can efficiently produce adversarial samples without the need of iterative updates or guidance from the surrogate classifiers. Extensive experiments demonstrate that the proposed end-to-end method outperforms baseline approaches in terms of both transferability and efficiency, while also maintaining notable advantages even against defense strategies.

The preprint can be found here: https://arxiv.org/abs/2503.00063

Code and model are available here: https://github.com/cognaclee/nopain

I’m so pleased to share that Prof. Huang Di, along with Prof. Yunhong Wang and Ass.Prof. Hongyu Yang, has been awarded the First Prize in Natural Science at the 2024 Wu Wenjun Artificial Intelligence Science and Technology Awards—the highest national recognition in intelligent science and technology in China.

Their research, "Efficient Representation Learning for Complex Visual Tasks", makes important theoretical and practical contributions by overcoming efficiency bottlenecks in visual representation learning across model design, data usage, and cross-domain transfer. The work has already seen impact across key sectors and has been praised for its simplicity, efficiency, and performance.

I am especially proud of this achievement because Prof. Huang Di completed his PhD under my supervision in 2010 within the Liris lab at Ecole Centrale de Lyon along with Prof.Yunhong Wang, and it’s been a joy to see him grow into a leading researcher. I also value the long-standing scientific collaboration I’ve had with the group of Prof. Yunhong Wang and Prof. Di Huang—their continued excellence and dedication to advancing AI is so fruitful in our joint research topics.

Congratulations to the entire team! This is a remarkable milestone and a strong step forward for the AI research community.

Pleased to share our last research entitled "Noise-Optimized Conditional Diffusion for Domain Adaptation (NOCDDA)", which has just been accepted in IJCAI, Montréal, August 2025

Domain adaptation is essential to enable models to generalize across shifting real-world conditions—without the burden of labeling every new domain. In this work, we tackle a core challenge in unsupervised domain adaptation (UDA) — the limited availability of high-confidence pseudo-labeled target samples. We introduce a conditional diffusion framework that bridges generative modeling and cross-domain decision-making, with a strong emphasis on class-aware noise optimization. The preprint can be found here: https://arxiv.org/abs/2505.07548v1

The International Joint Conference on Artificial Intelligence (IJCAI) is one of the premier global conferences in AI, bringing together top researchers from academia and industry since 1969. With a highly competitive review process and a strong focus on cutting-edge innovations across all areas of artificial intelligence, IJCAI has remained the premier conference bringing together the international AI community. This year’s edition will be held in Montréal, Canada, continuing its tradition of fostering high-impact AI research and collaboration.

Our former PhD student, Prof.Di Huang at Beihang University,  nominated as Young scientific talent and China-France Outstanding Young Scientific Researcher 2024, is giving a seminar On 20/12/2024 at 14:00 to 15:00. Salle Szulkin, bâtiment E6, 1er étage, Ecully - Visio : https://ec-lyon-fr.zoom.us/j/97959739701

With the rapid development of the internet and sensing technology, different mo-dalities of data are emerging at an unprecedented speed. Multimodal data convey more comprehensive clues, but due to significant differences in their underlying mechanisms and forms of expression, and the complexity of the coupling relation-ships between modalities, how to utilize and mine the complementary information of multi-modal data has become an important research direction in the field. This talk focuses on visual perception based on multi-modal data and introduces the recent research work of the team of the presenter in this direction. The key discussion point is mainly on the fusion of texture and geometric information, involving data collected from various types of devices such as image sensors, depth sensors, LIDAR, for diverse tasks, including, facial analysis, robotic grasping, object detection, with the applications in industry, transportation, and other related areas.

With 100+ papers in the major academic journals and conferences such as IEEE-TPAMI, IJCV, CVPR, ICCV, ECCV, and 12,000+ citations and H-index 50, Prof.Di Huang has been among the top 2% world-wide AI scientist list since released in 2019 (by Stanford University and Elsevier). 

« Interactive Data Visualization: Exploring and Struturing New Design Spaces», a Habilitation to Dirige Research of Ecole Centrale de Lyon, defended on 7-October-2024. Chair of the jury composed of Prof.Miriah MEYER (Linköping University-Sweden, Rapporteur), Dr.Emmanuel PIETRIGA (Research Director at INRIA, Rapporteur), Prof. Gennady ANDRIENIKO (Fraunhofer-IAIS Schloss Birlinghoven-Germany, Rapporteur), Dr. Michaël AUPETIT  (Senior scientist at Qatar Computing Institut - Quatar, INRIA), Prof.Claudio SILVA (New York University - United States), and Prof.Angela BONIFATI (Université Claude Bernard Lyon 1).

News at the INS2I@CNRS: https://www.ins2i.cnrs.fr/fr/cnrsinfo/liming-chen-apprend-aux-robots-manipuler-des-objets-mous-et-deformables 

A PhD thesis on “Taking advantage of new technologies, from deep learning to spatial omics: Towards a new clinically relevant morpho-molecular classification of pulmonary neuroendocrine tumors”, defended on 21-12-2023, in front of the jury composed of Prof.Delphine Maucort-Bouch (Chair, University Lyon 1 Claude Bernard), Prof.John Le Quesne (Rapporteur, University of Glasgow), Prof.Ruan Su (Rapporteur, University of Rouen), Dr.Garcia-Carbonero Rocio (Examinator, University of Madrid), Prof.Julien Mazières (Examinator, University of Toulouse 3), Dr.Lynnette Fernandez-Cuesta (Supervisor, IARC), Dr.Mathieu Foll (Co-supervisor, IARC), Prof.Liming Chen (Co-supervisor, ECL Liris)

Very honored to receive the 2022 award of applied research from FIEEC-Bpi France at the annual BIG (Bpi France Innovation Generation) event which gathered more than 65k participants and pretends to be the greatest business gathering in Europe. President Macron made the opening speech. This award rewards the research works that my group has carried out in partnership with Siléane, a leading French company in industrial robotics, to make their robots of manipulation more agile, more adaptive, more flexible. The award ceremony can be found on YouTube here.

Very pleased that our last collaborative research work on ImFace was presented today #CVPR2022. ImFace is a novel nonlinear 3D morphable face model with implicit neural representations mainly developed within Prof.Di Huang's group at #Beihang.

It builds two explicitly disentangled deformation fields to

model complex shapes associated with identities and expressions. It is a huge breakthrough compared to our last double linear 3D morphable model disentangled into identities and faciale expressions which appeared in #CVPR 2014.  The paper can be found here ; the video presentation here ; and the code here.

Dimitri Gominski, our former doctoral student, currently in postdoc at the University of Copenhagen, won the Best paper award at the earthvision 2022 workshop in conjunction with the CVPR'2022 conference, with the paper entitled "Cross-dataset learning for generalizable land use scene classification". A paper which relates to his thesis work iunder co-supervision of Dr.Valérie Gouet-Brunet at the National Institute of Geography (IGN).

In this work, we lay the foundations towards an "universal" model for land-use mapping. We found that our data-driven approach leverages inter-dataset variety during training and intra-class similarities during testing yields good performance on 8 datasets, sometimes better than ad-hoc few-shot or cross-domain methods!

For more details, read our article here: https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/html/Gominski_Cross-Dataset_Learning_for_Generalizable_Land_Use_Scene_Classification_CVPRW_2022_paper.html

A thesis funded by CSC and ANR on GAN-based Face Image Synthesis and its Application to Face Recognition. Started on 12 March 2018 and defended on 08-June-2022, in front of the jury composed of: Prof. Liming CHEN (Director of the thesis, ECL), Prof.Di HUANG (Co-Supervisor, Beihang University), Dr.Stéphane GENTRIC (IDEMIA, Examiner), Prof.Boulbaba BEN AMOR (Rapporteur, Telecom Lille), Dr.Sébastien Marcel(Rapporteur, Idiap), Prof.Alice Caplier (Chair, GIPSA Lab), Dr.Antitza Dantcheva (INRIA, Examiner).

Very pleased to learn yesterday and very honored to be nominated as a senior member at the IUF (Institut Universitaire de France) from October 2022 through the chair of innovation for the research project entitled "Look, Touch and Manipulate: Simulation empowered Self-supervised learning through self-play for dexterous robotic manipulation using Vision and Tactile Feeling", which has been judged as having high scientific value and strong potential for innovative development. #research #robotlearning #learning #IUF#

For further details, visit https://www.iufrance.fr/detail-de-lactualite/295.html

Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. However, finding visual sources covering a given area within a large mass of archives can be very difficult if they are poorly documented. In the case of aerial photographs, most of the time, this task is carried out by solely relying on the visual content of the images. Convolutional Neural Networks are capable to capture the visual cues of the images and match them to each other given a sufficient amount of training data. However, over time and across seasons, the natural and man-made landscapes may evolve, making historical image-based retrieval a challenging task. We want to approach this cross-time aerial indexing and retrieval problem from a different novel point of view: by using geometrical and topological properties of geographic entities of the researched zone encoded as graph representations which are more robust to appearance changes than the pure image-based ones. Geographic entities in the vertical aerial images are thought of as nodes in a graph, linked to each other by edges representing their spatial relationships. To build such graphs, we propose to use instances from topographic vector databases and state-of-the-art spatial analysis methods. We demonstrate how these geospatial graphs can be successfully matched across time by means of the learned graph embedding. Find the all story in our last publication here: https://www.mdpi.com/2220-9964/11/2/97 

A thesis funded by the Commissariat A l’Energie Atomique (CEA). PhD thesis on Deep Learning applied to multidimensional temporal signals. Started on 14-Dec-2018, and defended on 17-Dec-2021, in front of the jury composed of Prof.Liming Luke Chen (Chair, Ulster University), Dr.Hongying Meng (Rapporteur, reader at Brunel University London), Prof.Zhao Xi (Rapporteur, Xi’an Jiaotong University), Dr.Alice Othmani (Associate Prof, University of Paris Est), Dr. Marielle Malfante (Research engineer, CEA-LIST/DSCIN/LIIM), Prof.Liming Chen (Thesis director, ECL Liris), M.Andreas Vassilev (Co-supervisor, CEA/DSIS/SCSE/LSCM).

Deep neural networks have revolutionized Machine Learning, completely reshaping several domains of research in a mere decade. The most impressive changes were for domains like computer vision, for which deep learning outclassed the previous approaches based on handcrafted features. For instance, to the current day (end of 2021), the most baseline approach to extract features from a RGB image is to use a neural network trained on the popular image classification task ImageNet. More generally, in the most popular domains (Computer Vision, NLP, \textit{etc.}), there is a great deal of literature, good practices, and pretrained models accessible with a few lines of Python code. However, not all problems are as crowded as computer vision.

In some cases, a single laboratory can deal with multiple types of sensors recording temporal signals (accelerometers, strain sensors, GPS signals, physiological sensors) to perform Machine Learning on, in the span of a few months. For many unnoticed tasks, deep neural networks require considerable work: constitute a database, choose the type of preprocessing, hyperparameter selection, choice of an encoding, or even sensor choice, depending on the problem. We will take the place of a practitioner and review the most common choices we would have to make in order to make deep neural networks work with temporal signals. In each case, we will give indications the best choice to make, according to our experiments and/or the literature. Most of our experiments will focus on Transport Mode Detection, but we will use the literature to distinguish between the conclusions that only apply to our problem, and the affirmations that generalize elsewhere.

The defense of my 43rd thesis is under way...a thesis supported by ANR through the #Alegoria project and #DGA on Generalizable features and image search for multi-source interconnection and analysis by Dimitri Gominski, in front of the jury composed of  Prof. Peter BELL (Rapporteur, Friedrich-Alexander Universität, Erlangen-Nürnberg, Germany), Prof. Philippe JOLY (Rapporteur, Université Paul Sabatier, Toulouse, France), Prof. Jantien STOTER (Chair, Delft University of Technology, Netherland), Prof. Dimitris SAMARAS (Stony Brook University, USA),  Dr. Valérie GOUET-BRUNET (Supervisor, Senior researcher, Université Gustave Eiffel, France),  Prof. Liming CHEN (Co-supervisor, École Centrale Lyon, France)

“Giving Robots Human Dexterity: simulation empowered object instance aware self-supervised learning for adaptive bin-picking”, keynote speech at The International Conference on Emerging Techniques in Computational Intelligence, http://www.ietcint.com/, technically co-sponsored by the IEEE Computational Intelligence Society, Ecole Centrale School of Engineering, Mahindra University, Hyderabad, India, on Aug 25-27, 2021

We are pleased that our Aristotle project has been just accepted within the 1st Franco-German joint call on AI.  It teams up my group with Prof.Jan Peters' group at Technische Universität Darmstadt.

Dexterous manipulation has been a long-standing challenge in AI and robotics. Already Aristotle noted that the hand is the “tool of tools”, and Anaxagoras held that “man is the most intelligent of the animals because he has hands”. Thus, intelligence has long been understood to come together with dexterity, and the lack thereof in artificial systems explains why current robots are mostly limited to pre-programmed tasks in structured environments. In this project named after Aristotle, we aim to develop a new generation of dexterous robotic manipulation systems endowed with a pair of three-fingered hands, human-like vision and tactile sensing, and more importantly, the capacity for never-ending learning and inference to deal with ever more complex manipulation tasks. To achieve this goal, the Aristotle project requires the following components of increasing level of complexity: representation learning for tactile-driven finger control for in-hand manipulation, visuotactile hand-eye calibration fusing tactile and visual sensing modalities, dual-arm coordination that combines representations from the left and right hand, and finally, never-ending lifelong learning to capitalize on the previously learned manipulation skills. As such, the Aristotle project challenges the current 2nd-wave AI paradigm centered on learning from static human-curated datasets, and asserts itself as a 3rd-wave AI initiative by targeting human-level robotic dexterity. The Aristotle project stands out from traditional research proposals thanks to a deep and harmonious 40-year-long partnership between Ecole Centrale de Lyon (ECL) and Technische Universität Darmstadt (TUDa), conducive to forming a joint French-German AI research lab between two world-class AI research teams with complementary expertise in computer vision and robotics, thereby establishing an open platform for collaboration between other AI faculty at both institutions and further European partners. For this purpose, the Aristotle project proposes two co-supervised PhD theses and an annual 4-day open workshop, fostering scientific exchange and Europe-wide collaboration among AI researchers.

Congratulations to my PhD student, Amaury Depierre, for the acceptance of his submission to #ICRA2021 entitled "Optimizing correlated graspability score and grasp regression for better grasp prediction". The video showing the work is here https://youtu.be/KqsIRPL4r1Y; The preprint can be found here: https://arxiv.org/pdf/2002.00872.pdf

In this paper, we extend a state-of-the-art neural network with a scorer which evaluates the graspability of a given position and introduce a novel loss function which correlates regression of grasp parameters with graspability score. We show that this novel architecture improves the performance from 81.95% for a state-of-the-art grasp detection network to 85.74% on Jacquard dataset. Because real-life applications generally feature scenes of multiple objects laid on a variable decor, we also introduce Jacquard+, a test-only extension of Jacquard dataset. Its role is to complete the traditional real robot evaluation by benchmarking the adaptability of a learned grasp prediction model on a different data distribution than the training one while remaining in totally reproducible conditions. Using this novel benchmark and evaluated through the Simulated Grasp Trial criterion, our proposed model outperforms a state-of-the-art one by 7 points.

Very pleased that our paper on a 3D GAN for Improved Large_pose facial recognition has been accepted in CVPR 2021. An exemplary cooperation between IDEMIA and ECL Liris ! In this work we incorporate a 3D morphable model into the generator of a GAN in order to learn a nonlinear texture model from in-the-wild images. This allows generation

of new, synthetic identities, and manipulation of pose and expression without compromising the identity. Our synthesised data is used to augment training of facial recognition networks with performance evaluated on the challenging CFPW and Cross-Pose LFW datasets. You can  find all the details here:   https://arxiv.org/pdf/2012.10545.pdf

Annual (virtual) meeting of the Alegoria project which aims at valorizing digitized geographic iconographic heritage, in partnership with IGN, French National Archives, the Nicéphore Niépce Museum, Lavue. Find the whole story: http://alegoria.ign.fr/

The announcement: I'm pleased to invite you to the defence of my PhD thesis, which is entitled: "Data-augmentation with Synthetic Identities for Robust Facial Recognition ".

The defence will take place on Monday 14th December at 3:00 pm (this afternoon!) by videoconference that you be able to watch at the following link:

https://www.youtube.com/channel/UCP12DAKq0l9R35cISaiGmEg/live

The jury is composed of:

Liming CHEN, Director of the thesis, Professor, ECL

Sami ROMDHANI, Supervisor, Doctor, IDEMIA

Stéphane GENTRIC, Supervisor, Doctor, IDEMIA

Dimitris SAMARAS, Rapporteur, Professor, Stony Brook University

Boulbaba BEN AMOR, Rapporteur, Professor, Université de Lille

Bernadette DORIZZI, Examiner, Professor, Institut Télécom, Télécom SudParis

Ioannis KAKADIARIS, Examiner : Professor, University of Houston

Abstract (EN):

In 2014, use of deep neural networks (DNNs) revolutionised facial recognition (FR). DNNs are capable of learning to extract feature-based representations from images that are discriminative and robust to extraneous detail. Arguably, one of the most important factors now limiting the performance of FR algorithms is the data used to train them. High-quality image datasets that are representative of real-world test conditions can be difficult to collect. One potential solution is to augment datasets with synthetic images. This option recently became increasingly viable following the development of generative adversarial networks (GANs) which allow generation of highly realistic, synthetic data samples. This thesis investigates the use of GANs for augmentation of FR datasets. It looks at the ability of GANs to generate new identities, and their ability to disentangle identity from other forms of variation in images. Ultimately, a GAN integrating a 3D model is proposed in order to fully disentangle pose from identity. Images synthesised using the 3D GAN are shown to improve large-pose FR and a state-of-the-art accuracy is demonstrated for the challenging Cross-Pose LFW evaluation dataset.

The final chapter of the thesis evaluates one of the more nefarious uses of synthetic images: the face-morphing attack. Such attacks exploit imprecision in FR systems by manipulating images such that they might be falsely verified as belonging to more than one person. An evaluation of GAN-based face-morphing attacks is provided. Also introduced is a novel, GAN-based morphing method that minimises the distance of the morphed image from the original identities in a biometric feature-space. A potential counter measure to such morphing attacks is to train FR networks using additional, synthetic identities. In this vein, the effect of training using synthetic, 3D GAN data on the success of simulated face-morphing attacks is evaluated.

While robots in various forms are increasingly penetrating our daily life, e.g., automation of repetitive and penible tasks in industry, their use is mostly limited to specific tasks within known environment because of the complexity of the design of robots’ controllers even for very simple operations for humans, e.g., pouring a cup of tea. The CHIRON project aims to develop an AI empowered general purpose robotic system for dexterous manipulation of complex objects in unknown environments. This will be achieved through intuitive embodied robotic teleoperation for shared-controlled tele-manipulation tasks. Although such a system could be used in many applications involving complex manipulation of unknown objects in unstructured or hasardeuse environments, e.g., disasters, space or underwater exploration, the privileged use case within the CHIRON project is assistance for manipulation tasks, e.g.,  fetching a bottle of water, pouring it into a glass, bringing a new magazine from a shelf,   to help “stick-to-bed” patients or elders in their daily life, through an intuitive and embodied robot tele-operated by themself.

The CHIRON project gathers three partners with world class expertise in their complementary AI domains. Prof. Hasegawa’s group brings its unique expertise in the design and embodiment of an extra robotic thumb and intelligent cane for elderly. His challenge within the project is to go much further and design a simple and intuitive VR-based interface to enable a non expert operator to seamlessly manipulate objects through the dual-arm robot and visual and tactile feedback and achieve robotic embodiment without destruction. Prof. Peters’ team is providing their well known rich experience in robotc manipulation, including tactile sensing, robotic tele-operation, learning by demonstration, reinforcement learning. Their challenge in the project is to design a smart dual-arm robot controller sharing the control with the tele-operator however without explicit models on the object, its environment, grippers with tactile sensors and the dual-arm robot. Prof. Chen’s group is bringing their confirmed expertise in computer vision and machine learning for deep understanding of the scene for object manipulation. Their challenge is to make effective object segmentation with their pose and grasp position only with very few data for learning. For this purpose they are investigating few shot learning from the perspective of meta-learning. 

In collaboration with EXCOFFIER FRERES and SILEANE, two leading French companies in waste recycling and industrial robotics, I am very pleased to announce that we are going to work on AI enhanced robotised waste sorting for better environment protection within the FAIR WASTES project, a French government supported project worth of 3.73 M€ for the full investment.  My group will be involved in the design of AI algorithms, e.g.,  object instance segmentation and recognition, recognition of materials, AI enhanced grasping. My group at ECL Liris is funded  377 K€ within the project. 

Very pleased to annonce that our last work on instance segmentation and layout prediction from a single RGB image will soon appear in International Journal of Computer Vision (IJCV). It was a research work by our former PhD student, Matthieu Grard through an industrial thesis in collaboration with Siléane, a Saint-Etienne-based SME specialised in industrial robotics. In this work, we have proposed a multicameral design of deep auto-encoder composed of subtask-specific lightweight decoder and encoder-decoder units. For further details, please read the preprint here: https://lnkd.in/f2YMupMg

The workshop is taking place on June 4th, 2019 at CNAM within the framework of the open lab GDR ISIS. Please find the full program here. 

This book constitutes the refereed proceedings of the 7th International Workshop on Representations, Analysis and Recognition of Shape and Motion from Imaging Data, RFMI 2017, held in Savoi, France, in December 2017.

Find more here: https://www.springer.com/us/book/9783030198152

The objective of this workshop is to present and discuss the latest and most significant trends in the analysis, structuring, and understanding of multimedia contents dedicated to the valorization of heritage, with the emphasis on the unlocking of and access to the big data of the past.

for further details, visit  https://sumac2019.ec-lyon.fr 

I have given a talk on "giving eyes and intelligence to grasping robots" and attended to his group weekly meeting

I gave a seminar on GAN and Its Applications at Meetup and Lyon Data Science  on 12-Mar-2019...https://www.meetup.com/fr-FR/Lyon-Data-Science/events/259498325 

Une percée majeure en machine learning est l'apparition récente des réseaux antagonistes génératifs ou en anglais Generatif Adversarial Networks (GANs), qui permet de simuler des données , e.g., visages, pratiquement impossibles de différencier de vraies données. Ses applications sont aussi nombreuses que diversifiées, allant de l'édition de photos jusqu'à la traduction automatique en passant par le transfert de style. Dans cet exposé, Prof. Chen va introduire les principes de base des GANs , en décrire quelques applications , et donner un aperçu de ses derniers travaux, notamment de l'algorithme IVI-GAN, qui permet d'isoler des facteurs de variation dans la génération de données.

Dr. Liming Chen est Professeur à l'Ecole Centrale de Lyon où il mène un groupe de recherche sur la vision par ordinateur, le machine learning et la robotique depuis de nombreuses années. Son groupe a été lauréat des médailles d'or et d'argent du challenge ImageClef sur l'annotation automatique d'images en 2011 et de médaille d'or au challenge Shrec 3D face recognition en 2012. Leurs travaux de recherche ont trouvé de nombreuses applications, e.g., Morphoway, en biométrie pour le portail de passage automatique à l'aéroport, ou encore en bras de manipulation robotique, en partenariat avec des acteurs industriels majeurs.

Références :

- Generative Adversarial Nets, Goodfellow : https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

- Conditional Generative Adversarial Nets, Mirza : https://arxiv.org/pdf/1411.1784.pdf

- Intra-class Variation Isolation in Conditional GANs, Chen : https://arxiv.org/pdf/1811.11296.pdf

The second workshop on Face and Gesture Analysis for Health Informatics (FGAHI) will be held in conjunction with CVPR 2019, June 16th - June 21st, Long Beach, CA. The workshop aims to discuss the strengths and major challenges in using computer vision and machine learning of automatic face and gesture analysis for clinical research and healthcare applications. We invite scientists working in related areas of computer vision and machine learning for face and gesture analysis, affective computing, human behavior sensing, and cognitive behavior to share their expertise and achievements in the emerging field of computer vision and machine learning based face and gesture analysis for health informatics.

Full information available here: http://fgahi2019.isir.upmc.fr/ 

Weaving Ubiquitous Sensing and Computing into Ubiquitous Intelligence

Ubiquitous sensors, devices, networks and information are paving the way towards a smart world in which computational intelligence is distributed throughout the physical environment to provide reliable and relevant services to people. This ubiquitous intelligence will change the computing landscape

because it will enable new breeds of applications and systems to be developed and the realm of computing possibilities will be significantly extended. By enhancing everyday objects with intelligence, many tasks and processes could be simplified, more efficient and more enjoyable. Ubiquitous computing is to create such intelligent/smart environments, services and applications.

The 16th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2019) will

include a highly selective program of technical papers, accompanied by workshops, demos, panel

discussions and keynote speeches. We welcome high quality papers that describe original and

unpublished research advancing the state of the art in ubiquitous intelligence and computing.

With Dr.Emmanuel Dellandréa, we are giving a conference on Deep learning for Detecting and Segmenting Objects in the cycle Vision, Image and AI within the Open University of Lyon. 1.

09-APR-2019, at 5-7 pm, Amphithéâtre (rdc), ENSSIB, 17-21 Boulevard du 11 Novembre 1918, 69100 Villeurbanne

In this survey, we propose to discuss knowledge transfer works for vision recognition tasks, and to explore the common rules behind these works. As the primary goal of knowledge transfer methods is to harness learned knowledge for re-use in novel learning tasks, we overview in this survey knowledge transfer methods from the viewpoint of the knowledge being transferred. Specifically, We will firstly discuss the reusable knowledge in a vision recognition task, then we will categorize different kinds of knowledge transfer approaches by where the knowledge comes from and where the knowledge goes to. We aim at finding general rules across different Transfer Learning (TL) settings instead of focusing on their particularities. This viewpoint is in clear contrast to previous surveys on TL. However, any existing method has its own scope of applicability, and we will indicate the applicable scenarios when introducing each method.

See the details here: https://hal.archives-ouvertes.fr/hal-02101005 

The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. CiteScore:8.7; IF:3.121. 

This is made possible thanks to our latest research work on IVI-GAN. Find the full story  here: Intra-class Variation Isolation in Conditional GAN