Vision Transformer Adapters for Generalizable Multitask Learning

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Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data's inherent structure or context by solving a pretext task. With ...

Now is an exciting time for the domain of Earth observation (EO), with a multitude of diverse sensors looking at the planet from satellites, airplanes or drones. The volume of imagery acquired is massive, and hold great potential for a variety of applicati ...

From the nineteenth-century panorama to the emergence of the digital panoramic format in the 's, the visualization of large images frequently relies on panoramic viewing strategies. Originally rendered in the form of epic painted canvases, these strategies ...

Recent years have seen remarkable advancements in saliency estimation methods, mainly due to deep learning models leveraging the widespread availability of real-world images. However, saliency is profoundly shaped by the intricacies of the human visual att ...

Current procedures for the rapid inspection of buildings and infrastructure are subjective, time-consuming, and cumbersome to document, necessitating new technologies to automate the process and eliminate these shortcomings. Fortunately, recent development ...

Over the years, clinical institutes accumulated large amounts of digital slides from resected tissue specimens. These digital images, called whole slide images (WSIs), are high-resolution tissue snapshots that depict the complex interaction of cells at the ...

We present an approach to bridge the gap between the computational models of human vision and the clinical practice on visual impairments (VI). In a nutshell, we propose to connect advances in neuroscience and machine learning to study the impact of VI on ...

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

Most modern in-memory online transaction processing (OLTP) engines rely on multi-version concurrency control (MVCC) to provide data consistency guarantees in the presence of conflicting data accesses. MVCC improves concurrency by generating a new version o ...

Self-attention mechanisms and non-local blocks have become crucial building blocks for state-of-the-art neural architectures thanks to their unparalleled ability in capturing long-range dependencies in the input. However their cost is quadratic with the nu ...

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive ...

Dense image-based prediction methods have advanced tremendously in recent years. Their remarkable development has been possible due to the ample availability of real-world imagery. While these methods work well on photographs, their abilities do not genera ...

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 ...

Transportation, which deals with moving people and goods around, has a clear impact on the economic development of our society and our well-being. Traditionally, transportation was studied and analyzed using expensive sensors, such as induction loops, that ...

The aim of this paper is to serve as a lightweight introduction to concurrency control for database theorists through a uniform presentation of the work on robustness against Multiversion Read Committed and Snapshot Isolation. ...

Visual Question Answering (VQA) on remote sensing imagery can help non-expert users in extracting information from Earth observation data. Current approaches follow a neural encoder-decoder design, combining convolutional and recurrent encoders together wi ...

Training deep neural network based Automatic Speech Recognition (ASR) models often requires thousands of hours of transcribed data, limiting their use to only a few languages. Moreover, current state-of-the-art acoustic models are based on the Transformer ...

Recent traffic flow prediction methods are lacking abilities to determine predictive features. Thus, they will propagate the error in the next timestamps. In this paper, first, we assess the role of spatial and temporal features on the traffic speed predic ...

Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints. We introduce DISK (DIScrete Keypoints), a novel method that overcomes these obstacles by leveragin ...

Vision Transformer Adapters for Generalizable Multitask Learning

Chattez avec Graph Search

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification

Interact with Earth Observation Images using AI: Transparent Methods and Evaluation for Visual Question Answering

Cultural big data: nineteenth to twenty-first century panoramic visualization

Visual Saliency Prediction for Natural Images, Comics Panels, and Comics Pages

Damage-augmented digital twins towards the automated inspection of buildings

Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer

Modeling Visual Impairments with Artificial Neural Networks: a Review

Modeling Structured Data in Attention-based Models

One-shot Garbage Collection for In-memory OLTP through Temporality-aware Version Storage

Linear Complexity Self-Attention With 3rd Order Polynomials

Transformer Models for Vision

Dense Image-based Predictions for Comics Analysis

Learning to Represent and Reconstruct 3D Deformable Objects

Visual Scene Understanding for Transportation: From Detecting Objects To Relationships

Concurrency control for database theorists

Remote sensing visual question answering with a self-attention multi-modal encoder

Efficient Transformer-Based Speech Recognition

STATNet: Spatial-temporal attention in the traffic prediction

DISK: learning local features with policy gradient