TILDE: A Temporally Invariant Learned DEtector

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Remote sensing visual question answering (RSVQA) opens new avenues to promote the use of satellites data, by interfacing satellite image analysis with natural language processing. Capitalizing on the remarkable advances in natural language processing and c ...

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume ...

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging. Being able to study this incredible agility will be critical for the development of n ...

Unsupervised self-rehabilitation exercises and physical training can cause serious injuries if performed incorrectly. We introduce a learning-based framework that identifies the mistakes made by a user and proposes corrective measures for easier and safer ...

Correspondence pruning aims to correctly remove false matches (outliers) from an initial set of putative correspondences. The pruning process is challenging since putative matches are typically extremely unbalanced, largely dominated by outliers, and the r ...

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...

The learning process depends on the nature of the learning environment, particularly in the case of open-ended learning environments, where the learning process is considered to be non-linear. In this paper, we report on the findings of employing a multimo ...

Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the perfor ...

Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural networks trained by minimizing voxel-wise loss functions that do not capture t ...

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 ...

Human-centered scene understanding is the process of perceiving and analysing a dynamic scene observed through a network of sensors with emphasis on human-related activities. It includes the visual perception of human-related activities from either single ...

Auditory perception is an essential part of a robotic system in Human-Robot Interaction (HRI), and creating an artificial auditory perception system that is on par with human has been a long-standing goal for researchers. In fact, this is a challenging res ...

Background and objective: Compared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI sequence, the specialized magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) shows a higher brain tissue and lesion con ...

We present a visual localization system that learns to estimate camera poses in the real world with the help of synthetic data. Despite significant progress in recent years, most learning-based approaches to visual localization target at a single domain an ...

Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to misl ...

Turn-on time is a key factor for lighting devices to be of practical application. To decrease the turn-on time value of a deep-red light-emitting electrochemical cells (DR-LECs), two novel approaches based on molecularly engineered ruthenium phenanthroimid ...

TILDE: A Temporally Invariant Learned DEtector

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Language Transformers for Remote Sensing Visual Question Answering

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

Improving the Training of Compact Neural Networks for Visual Recognition

AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild

3D Pose Based Feedback For Physical Exercises

Progressive Correspondence Pruning by Consensus Learning

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Temporal Pathways to Learning: How Learning Emerges in an Open-ended Collaborative Activity

Context-Aware Image Super-Resolution Using Deep Neural Networks

Enforcing Connectivity of 3D Linear Structures Using Their 2D Projections

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

Human-Centered Scene Understanding via Crowd Counting

Deep Learning Approaches for Auditory Perception in Robotics

MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients

CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data

Natural-Looking Adversarial Examples From Freehand Sketches

High-Efficiency Deep-Red Light-Emitting Electrochemical Cell Based on a Trinuclear Ruthenium(II)-Silver(I) Complex