Predicting values of Particulate Matter concentration presents an undeniable interest, as it is key in preventing their adversarial impact on human health. Physics-based methods rely on several different variables and equations to perform this task. They are reliable, as they are based on known physical laws and models. Neural networks rely on high computing power and an important volume of data. They are faster than physics-based models, but sometimes considered as black boxes and potentially less reliable. There is therefore a need for a fast and reliable solution. Physics-Informed Neural Networks make use of all four (variables, equations, computing power and a high volume of data), which makes them more reliable than classical neural networks, and faster than physics-based methods. This paper proposes a model that predicts Particulate Matter concentration values using a variety of meteorological and optical variables as well as a Physics-Inspired loss function. The impact of different factors on the performance of this model, such as the amount of available ground truth, the use of Boundary Conditions, and the prediction time frame, is discussed in this paper as well. The model shows satisfying performance. More precisely, when compared to the best baseline method presented, our model shows a diminution of the MAE from 3.74 to 2.71 .

Spiking Neural Networks (SNNs) are increasingly explored for their energy efficiency and biological plausibility, offering a compelling alternative to conventional artificial neural networks in neuromorphic applications. However, even Spik-former a fully spiking adaptation of the Transformer model, can exhibit significant computational redundancy due to excessive spike activity, resulting in non-negligible energy consumption. In this paper, we introduce Masked Spikformer, a unified framework for regulating temporal spike activity in spiking Transformers through three complementary masking strategies: Random Spike Masking (RSM), Gaussian-Based Spike Masking (GSM), and Gaussian-Based Spike Weighting (GSW). These approaches encompass both binary masking and continuous, learnable temporal weighting. Our method is integrated into fully spiking architectures and applied consistently during both training and inference. Experimental results on neuromorphic benchmarks (CIFARIO-DVS and DVS128 Gesture) show that the proposed masking strategies significantly reduce energy consumption while maintaining high classification performance. Notably, the best accuracy on DVS128 Gesture is achieved by RSM with 70% masking, while GSW(i), the inverse Gaussian weighting variant, attains the highest accuracy on CIFARIO-DVS. RSM also provides the lowest energy consumption across both datasets, highlighting the effectiveness of temporal sparsity for energy-efficient spiking Transformers.

This paper provides a comprehensive review of the integration of Spiking Neural Networks (SNNs) and Transformers, combining the energy efficiency of SNNs with the high performance of Transformer architectures. By leveraging the event-driven nature of SNNs and the powerful self-attention mechanism of Transformers, this fusion aims to address the challenges of high energy consumption in deep learning while improving task accuracy, especially for complex datasets. We introduce the core concepts of SNNs and Transformers, reviewing state-of-the-art methods for their combination, including hybrid architectures. The performance of each architecture is presented thanks to both static and neuromorphic datasets, highlighting their advantages and limitations. This review also discusses the challenges of integrating self-attention into spiking architectures and outlines future research directions to further enhance model performance and energy efficiency.

Facial Expression Recognition (FER) is an active research domain that has shown great progress recently, notably thanks to the use of large deep learning models. However, such approaches are particularly energy intensive, which makes their deployment difficult for edge devices. To address this issue, Spiking Neural Networks (SNNs) coupled with event cameras are a promising alternative, capable of processing sparse and asynchronous events with lower energy consumption. In this paper, we establish the first use of event cameras for FER, named "Event-based FER", and propose the first related benchmarks by converting popular video FER datasets to event streams. To deal with this new task, we propose "Spiking-FER", a deep convolutional SNN model, and compare it against a similar Artificial Neural Network (ANN). Experiments show that the proposed approach achieves comparable performance to the ANN architecture, while consuming less energy by orders of magnitude (up to 65.39x). In addition, an experimental study of various event-based data augmentation techniques is performed to provide insights into the efficient transformations specific to event-based FER.

Often, physical phenomena are difficult to model by a simple equation and require a lot of processing resources. Studies on Physics-Informed Neural Networks (PINNs) have repeatedly shown the interest of leveraging the information contained in context-relevant physics equations in order to guide the training, as well the ability of this type of networks to reduce the need for labeled data. Some of these analysis have also demonstrated the interest of additional knowledge through Initial and Boundary Conditions (I/BCs) in this type of context. This knowledge can take a variety of forms and shapes, among which is the one of sparse ground truths, and more precisely sparse matrices, as matrices are often well fitted to represent the spatial aspect of this type of problem. The popularity of Computer Vision techniques is partly due to their ability to take into account the spatial aspect of a given problem. The combined use of methods from these two fields therefore seems natural. This paper introduces a method for the use of Boundary Conditions for Generative Adversarial Networks (GANs), and outside the context of PINNs. The interest of leveraging the BCs with a GAN is evaluated in terms of performance, and various BC configuration and quantities are tested to discuss their impact on obtained performance.

This paper presents "Bina-Rep", a simple representation method that converts asynchronous streams of events from event cameras to a sequence of sparse and expressive event frames. By representing multiple binary event images as a single frame of N-bit numbers, our method is able to obtain sparser and more expressive event frames thanks to the retained information about event orders in the original stream. Coupled with our proposed model based on a convolutional neural network, the reported results achieve state-of-the-art performance and repeatedly outperforms other common event representation methods. Our approach also shows competitive robustness against common image corruptions, compared to other representation techniques.

Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular public datasets, proposes a categorization of the strategies employed by recent contributions, evaluates the performance of the current state-of-the-art, and discusses the remaining challenges and promising directions for future works.

With the advent of neuromorphic hardware, spiking neural networks can be a good energy-efficient alternative to artificial neural networks. However, the use of spiking neural networks to perform computer vision tasks remains limited, mainly focusing on simple tasks such as digit recognition. It remains hard to deal with more complex tasks (e.g. segmentation, object detection) due to the small number of works on deep spiking neural networks for these tasks. The objective of this paper is to make the first step towards modern computer vision with supervised spiking neural networks. We propose a deep convolutional spiking neural network for the localization of a single object in a grayscale image. We propose a network based on DECOLLE, a spiking model that enables local surrogate gradient-based learning. The encouraging results reported on Oxford-IIIT-Pet validates the exploitation of spiking neural networks with a supervised learning approach for more elaborate vision tasks in the future.

The use of e-service platforms has become essential for many applications (administrative documents, online shopping, reservations). Although these platforms have improved significantly the user experience, unexpected and stressful situations can occur. Navigation problems (latency, missing information, poor ergonomics) are not always reported to the designers. To address this problem, we propose a multimodal dataset (video, audio, and physiological data) to help implicitly quantify the impact of navigation problems on users when using an e-service platform. A scenario has been designed to generate various navigation problems which can lead to changes in user behaviour. A baseline is proposed to spot changes in user behaviour, opening the way towards automatically qualifying user experiences while using e-service platforms.

In uncontrolled settings occlusions occur and interfere with facial expressions recognition task. It is interesting to limit the number of regions required for face expression recognition task in order to moderate the occlusion interference. We propose a weighting scheme that ranks the facial regions needed to recognize expressions. Weights are calculated based on the contribution of each region to boost recognition in presence of various occlusions. Intelligent facial frameworks, based on region ranks are computed in presence of static occlusions (such as glasses, hair, hand on the face). Evaluations conducted using motion information as the underlying descriptor show that our approach maintains, per expression, very good recognition rates under various static occlusions occurring in uncontrolled settings.

This article relies on two recent developments of well known methods which are a color Fourier transform using geometric algebra and Generalized Fourier descriptors defined from the group M2of the motion of the plane. In this paper, new generalized color Fourier descriptors (GCFD) are proposed. They depend on the choice of a bivector B acting as an analysis plane in a colorimetric space. The relevance of proposed descriptors is discussed on several color image databases. In particular, the influence of parameter B is studied regarding the type of images. It appears that proposed descriptors are more compact with a lower complexity and better classification rate.