Human-Centered Scene Understanding via Crowd Counting

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Motion forecasting is crucial in enabling autonomous vehicles to anticipate the future trajectories of surrounding agents. To do so, it requires solving mapping, detection, tracking, and then forecasting problems, in a multi-step pipeline. In this complex ...

Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size dataset ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. However, centralized training, the most common paradigm for MARL, limits large-scale deployment i ...

To fully comprehend visual perception, we need to necessarily understand its temporal dimension. Our visual environment is highly dynamic, requiring the processing and integration of temporal signals in order to make sense of it. Many processes, such as th ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural networks capture d ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Recent work suggests that the individual alpha peak frequency (IAPF) reflects the temporal resolution of visual processing: individuals with higher IAPF can segregate stimuli at shorter intervals compared to those with lower IAPF. However, this evidence ma ...

Unmanned vehicles have become indispensable tools in various fields such as agriculture, environmental monitoring, and disaster response. A crucial component of these platforms is sensor fusion. As measurements are inherently noisy, certain variables canno ...

Foundation models have become a powerful tool in single-cell transcriptomics, enabling broad generalization across tasks such as cell type annotation, data integration, and drug response prediction. Yet, most current models are trained predominantly on hea ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

Purpose: Although clinical 7T MRI offers various advantages compared to lower field strengths, achieving spatially uniform flip angle distributions remains a challenge. Sampling Perfection with Application optimized Contrast using different flip angle Evol ...

Human-Centered Scene Understanding via Crowd Counting

Chattez avec Graph Search

Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Scalable Multi-agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

Temporal aspects of feature integration in vision

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Unveiling the complexity of learning and decision-making

A phase transition in diffusion models reveals the hierarchical nature of data

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Alpha peak frequency affects visual performance beyond temporal resolution

Factor-graph Optimisation with Vehicle Dynamic Models for Mapping & Sensing Applications

Abstract IA02: Cancer-specific foundation models: Friend or foe in healthcare AI?

Optimizing Dynamic Aperture Studies with Active Learning

Individually optimized dynamic parallel transmit pulses for 3D high‐resolution SPACE imaging at 7T