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Found 11 papers, 6 papers with code
Exploring Human-AI Perception Alignment in Sensory Experiences: Do LLMs Understand Textile Hand?
An important yet often overlooked aspect of this alignment is the perceptual alignment.
Multi-Modal Hybrid Learning and Sequential Training for RGB-T Saliency Detection
To address this, we first propose a Multi-Modal Hybrid loss (MMHL) that comprises supervised and self-supervised loss functions.
TextileNet: A Material Taxonomy-based Fashion Textile Dataset
At the same time, Net Zero is a global goal and the fashion industry is undergoing a significant change so that textile materials can be reused, repaired and recycled in a sustainable manner.
Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images
Limited availability of datasets from unconstrained settings further limits the use of the state-of-the-art segmentation networks, loss functions and learning strategies which have been built and validated for RGB images.
Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking
Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems.
Hearing through Vibrations: Perception of Musical Emotions by Profoundly Deaf People
Advances in tactile-audio feedback technology have created new possibilities for deaf people to feel music.
Human-Computer Interaction
Physiological and Affective Computing through Thermal Imaging: A Survey
Thermal imaging-based physiological and affective computing is an emerging research area enabling technologies to monitor our bodily functions and understand psychological and affective needs in a contactless manner.
Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging
Background: A smartphone is a promising tool for daily cardiovascular measurement and mental stress monitoring.
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments.
DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings
Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e. g., up to a few hours).
Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging
In this paper, we propose a novel and robust approach for respiration tracking which compensates for the negative effects of variations in the ambient temperature and motion artifacts and can accurately extract breathing rates in highly dynamic thermal scenes.
