Search Results

Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means for an ML model to be fair. This has been addressed by the ML community with ...

Buildings are highly responsible for total energy consumption in cities; therefore, accurate estimation of building energy consumption is essential for developing energy-efficient strategies on an urban scale. Data-driven urban building energy models ...

Mental health is becoming an increasingly prominent health challenge. Despite a plethora of studies analysing and mitigating bias for a variety of tasks such as face recognition and credit scoring, research on machine learning (ML) fairness for mental ...

Detecting objects in challenging illumination conditions is critical for autonomous driving. Existing solutions detect objects with standard or tone-mapped Low Dynamic Range (LDR) images. In this paper, we propose a novel adversarial approach ...

Object detectors are conventionally trained by a weighted sum of classification and localization losses. Recent studies (e.g., predicting IoU with an auxiliary head, Generalized Focal Loss, Rank & Sort Loss) have shown that forcing these two loss terms to ...

Given the increasing prevalence of facial analysis technology, the problem of bias in these tools is becoming an even greater source of concern. Causality has been proposed as a method to address the problem of bias, giving rise to the popularity ...

Audio Event Detection (AED) pertains to identifying the types of events in audio signals. AED is essential for applications requiring decisions based on audio signals, which can be critical, for example, for health, surveillance and security ...$$

• A vision-based approach using deep learning architectures to estimate people count.

Although occupancy information is critical to energy consumption of existing buildings, it still remains to be a major source of uncertainty. For reliable and accurate occupant modeling with minimal uncertainties, capturing precise ...

Ground-truth depth, when combined with color data, helps improve object detection accuracy over baseline models that only use color. However, estimated depth does not always yield improvements. Many factors affect the performance of ...

Most state-of-the-art approaches for Facial Action Unit (AU) detection rely on evaluating static frames, encoding a snapshot of heightened facial activity. In real-world interactions, however, facial expressions are more subtle and evolve over time ...

In this paper, we study the conventional and learning-based control approaches for multi-rotor platforms, with and without the presence of an actuated “tail” appendage. A comprehensive experimental comparison between the proven control-theoretic ...

While most of the research in Human-Robot Interaction (HRI) focuses on short-term interactions, long-term interactions require bolder developments and a substantial amount of resources, especially if the robots are deployed in the wild. Robots need to ...

We propose average Localisation-Recall-Precision (aLRP), a unified, bounded, balanced and ranking-based loss function for both classification and localisation tasks in object detection. aLRP extends the Localisation-Recall-Precision (LRP) performance ...

In this work, we combine 3D convolution with late temporal modeling for action recognition. For this aim, we replace the conventional Temporal Global Average Pooling (TGAP) layer at the end of 3D convolutional architecture with the Bidirectional ...

Robots collaborating with humans in realistic environments need to be able to detect the tools that can be used and manipulated. However, there is no available dataset or study that addresses this challenge in real settings. In this paper, we fill ...

Recognition of expressions of emotions and affect from facial images is a well-studied research problem in the fields of affective computing and computer vision with a large number of datasets available containing facial images and corresponding ...

In non-residential buildings, space lighting accounts for 17 % of the total energy consumption. Effective use of daylighting has great potential to reduce lighting energy use in buildings. The amount of daylighting through the building windows is ...

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is ...

Scene modeling is very crucial for robots that need to perceive, reason about and manipulate the objects in their environments. In this paper, we adapt and extend Boltzmann Machines (BMs) for contextualized scene modeling. Although ...

• Boltzmann Machines (BMs) are adapted and used for contextualized scene modeling.

Scene extrapolation is a challenging variant of the scene completion problem, which pertains to predicting the missing part(s) of a scene. While the 3D scene completion algorithms in the literature try to fill the occluded part of a scene such as a ...