27 accepted papers
Generalizable Neural Radiance Fields (GeNeRF) enable high-quality scene reconstruction from a limited number of views and can generalize to unseen scenes. However, in real-world environments, transient distractors disrupt structural consistency across views, leading to deviated supervision signals a
Using Quadrics as the object representation has the benefits of both generality and closed-form projection derivation between image and world spaces. Although numerous constraints have been proposed for dual quadric reconstruction, we found that many of them are imprecise and provide minimal improve
Large vision-language models (VLMs) have demonstrated remarkable abilities in understanding everyday content. However, their performance in the domain of art, particularly culturally rich art forms, remains less explored. As a pearl of human wisdom and creativity, art encapsulates complex cultural n…
Automating child speech analysis is crucial for applications such as neurocognitive assessments. Speaker diarization, which identifies "who spoke when", is an essential component of the automated analysis. However, publicly available child-adult speaker diarization solutions are scarce due to privac…
Modern smart glasses leverage machine learning to offer real-time transcriptions, considerably enriching human communication experiences. However, such systems frequently encounter challenges related to environmental noises, leading to decreased speech recognition. To improve voice quality, this wor…
Brain-computer interfaces (BCI) offer numerous human-centered application possibilities, particularly affecting people with neurological disorders. Text or speech decoding from brain activities is a relevant domain that could augment the quality of life for people with impaired speech perception. We…
Visual Place Recognition (VPR) enables robust localization through image retrieval based on learned descriptors. However, drastic appearance variations of images at the same place caused by viewpoint changes can lead to inconsistent supervision signals, thereby degrading descriptor learning. Exi…
Understanding speech production both visually and kinematically can inform second language learning system designs, as well as the creation of speaking characters in video games and animations. In this work, we introduce a data-driven method to visually represent articulator motion in Magnetic Reson…
Building accurate and reliable maps is a critical requirement for autonomous robots. In this paper, we propose UN3-Mapping, an implicit neural mapping method that enables high-quality 3D reconstruction with integrated uncertainty estimation. Our approach employs a hybrid representation: an implicit
Interactions involving children span a wide range of important domains from learning to clinical diagnostic and therapeutic contexts. Automated analyses of such interactions are motivated by the need to seek accurate insights and offer scale and robustness across diverse and wide-ranging conditions.…
Traditional music search engines rely on retrieval methods that match natural language queries with music metadata. There have been increasing efforts to expand retrieval methods to consider the audio characteristics of music itself, using queries of various modalities including text, video, and spe…
Significant advances are being made in speech emotion recognition (SER) using deep learning models. Nonetheless, training SER systems remains challenging, requiring both time and costly resources. Like many other machine learning tasks, acquiring datasets for SER requires substantial data annotation…
Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty related to range sensing, the impact of incident angle on uncer
Local geometric information, i.e., normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localizati
Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame. To improve the robustness of VPR in perceptually aliasing scenarios, sequence-based VPR methods are proposed. These methods are either based on matching between fra
Accurate and dense mapping in large-scale environments is essential for various robot applications. Recently, implicit neural signed distance fields (SDFs) have shown promising advances in this task. However, most existing approaches employ projective distances from range data as SDF supervision, in
Recent studies have explored using pre-trained embeddings for speech emotion recognition, achieving comparable performance to conventional methods that rely on low-level knowledge-inspired acoustic features. These embeddings are often generated from models trained on large-scale speech datasets usin…
In unmanned vehicle perception, dynamic object classification is applied to classify objects accurately and timely, providing decision-making for obstacle avoidance and planning. Low-resolution LiDAR is one of the most important sensors for this task. Unfortunately, the existing approaches perform u
Federated learning (FL) has gained substantial attention in recent years due to data privacy concerns related to the pervasiveness of consumer devices that continuously collect data from users. While a number of FL benchmarks have been developed to facilitate FL research, none of them include audio…
The sensitivity of data collected via ambulatory monitoring, which regularly involve the recording of speech signals and sensor information, can cause strong privacy concerns. We investigate user re-identification risk in a corpus of such data collected to observe the interplay between behavior, phy…
Speech Emotion Recognition (SER) techniques have gained considerable interest in many applications including smart virtual assistants and health state tracking. SER systems often acquire and transmit speech data collected at the client-side to remote cloud platforms for inference and decision making…
The scale ambiguity problem is inherently unsolvable to monocular SLAM without the metric baseline between moving cameras. In this paper, we present a novel scale estimation approach based on an object-level SLAM system. To obtain the absolute scale of the reconstructed map, we formulate an optimiza…
Herein, we present a novel approach for monocular dual quadric initialization that combines three-dimensional (3D) map points with two-dimensional (2D) object detection for forward-translating camera movements. The traditional approach using 2D detection bounding boxes in multiple views fails in str
Wearable sensors today can unobtrusively collect rich time-series of physiological states and human movement patterns over a prolonged period. Gaining a better understanding of how an individual's physiological responses vary in different workplace environments can be valuable in understanding human…
Continuously-worn wearable sensors provide an unprecedented opportunity to unobtrusively measure rich bio-behavioral time-series recordings in natural settings such as the workplace. These time-series data can be helpful in inferring broad patterns of behavior such as common routines and daily stres…
Continuously-worn wearable sensors produce copious amounts of rich bio-behavioral time series recordings. Exploring recurring patterns, often known as motifs, in wearable time series offers critical insights into understanding the nature of human behavior. Challenges in discovering motifs from weara…
Gaining a better understanding of how people move about and interact with their environment is an important piece of understanding human behavior. Careful analysis of individuals' deviations or variations in movement over time can provide an awareness about changes to their physical or mental state…