Two-handed gestures for human-computer interaction

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Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

Region extraction is a very common task in both Computer Science and Engineering with several applications in object recognition and motion analysis, among others. Most of the literature focuses on regions delimited by straight lines, often in the special ...

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high ...

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

Computers have long been augmenting human capabilities and communication, and with emerging technologies, physical immersion in virtual worlds is now attainable. These advancements push the boundaries of human potential, allowing designers, architects, eng ...

Jointly estimating hand and object shape facilitates the grasping task in human-to-robot handovers. Relying on handcrafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to dete ...

Atypical aspects in speech concern speech that deviates from what is commonly considered normal or healthy. In this thesis, we propose novel methods for detection and analysis of these aspects, e.g. to monitor the temporary state of a speaker, diseases tha ...

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or ...

Advances in soft sensors coupled with machine learning are enabling increasingly capable wearable systems. Since hand motion in particular can convey useful information for developing intuitive interfaces, glove-based systems can have a significant impact ...

Digital technologies reshape the way we interact with our environment, including with artworks. Advanced computational imaging solutions allow having extremely high-resolution digital reproductions of artworks outside museums, presumably increasing artwork ...

Representing and reconstructing 3D deformable shapes are two tightly linked problems that have long been studied within the computer vision field. Deformable shapes are truly ubiquitous in the real world, whether be it specific object classes such as human ...

The field of text-to-video retrieval has advanced significantly with the evolution of language models and large-scale pre-training on generated caption-video pairs. Current methods predominantly focus on visual and event-based details, making retrieval lar ...

A substantial body of research in multimodal interaction has studied how people naturally interact -face-to-face and through machinesand developed technology to analyze, support, and extend such forms of interaction. The talk will share personal experience ...

In this paper, we present EdgeFace - a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of both CNN and Transformer models, and a low rank linear layer, EdgeFace achi ...

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

Adaptive Social Learning (ASL) enables consistent truth learning in nonstationary environments. In this framework, agents linked by a graph, exchange their local beliefs with neighbors to track some underlying state of interest. This state can drift over t ...

This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference fro ...

Deep convolutional neural networks have shown remarkable results on face recognition (FR). Despite their significant progress, the performance of current face recognition techniques is often assessed in benchmarks under not always realistic conditions. The ...

Smart meters, devices measuring the electricity and gas consumption of a household, are currently being deployed at a fast rate throughout the world. The data they collect are extremely useful, including in the fight against climate change. However, these ...

The adoption of technology in urban participatory planning with tools such as Virtual Geographic Environments (VGE) promises a broader engagement of urban dwellers, which should ultimately lead to the creation of better cities. However, the authorities and ...

Two-handed gestures for human-computer interaction

Chat with Graph Search

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Region Extraction in Mesh Intersection

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

Human Interactive Robotic Surfaces for Physical Immersion

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

Novel Methods For Detection And Analysis Of Atypical Aspects In Speech

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

A Wearable Smart Glove and Its Application of Pose and Gesture Detection to Sign Language Classification

Interactive Digital Engagement With Visual Artworks and Cultural Artefacts Enhances User Aesthetic Experiences in the Laboratory and Museum

Learning to Represent and Reconstruct 3D Deformable Objects

Beyond Essentials: Nuanced and Diverse Text-to-video Retrieval

Focus on People: Five Qestions from Human-Centered Computing

EdgeFace: Efficient Face Recognition Model for Edge Devices

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Adaptive Social Learning for Tracking Rare Transition Markov Chains

Hidden Markov Modeling Over Graphs

A Novel Framework for Assessment of Deep Face Recognition Systems in Realistic Conditions

Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks

Experiencing virtual geographic environment in urban 3D participatory e-planning: A user perspective