Toward Automatic Typography Analysis: Serif Classification and Font Similarities

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Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remai ...

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

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 ...

Abstractive summarization has seen big improvements in recent years, mostly due to advances in neural language modeling, language model pretraining, and scaling models and datasets. While large language models generate summaries that are fluent, coherent, ...

Robustness of medical image classification models is limited by its exposure to the candidate disease classes. Generalized zero shot learning (GZSL) aims at correctly predicting seen and unseen classes and most current GZSL approaches have focused on the s ...

The way biological brains carry out advanced yet extremely energy efficient signal processing remains both fascinating and unintelligible. It is known however that at least some areas of the brain perform fast and low-cost processing relying only on a smal ...

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 ...

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 ...

Detecting people from 2D images and analyzing their motion in 3D have been long standing computer vision problems central to numerous applications such as autonomous driving and athletic training. Recently, with the availability of large amounts of trainin ...

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality leve ...

Despite their irresistible success, deep learning algorithms still heavily rely on annotated data, and unsupervised settings pose many challenges, such as finding the right inductive bias in diverse scenarios. In this paper, we propose an object-centric mo ...

Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions. However, despite the success of deep learning-based models applied to rasterized images, the problem of vector graphics representa ...

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances ...

Toward Automatic Typography Analysis: Serif Classification and Font Similarities

Chattez avec Graph Search

Aggregating Spatial and Photometric Context for Photometric Stereo

The multimodality cell segmentation challenge: toward universal solutions

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

CLIP the Gap: A Single Domain Generalization Approach for Object Detection

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Transformer Models for Vision

Interpretable Representation Learning and Evaluation for Abstractive Summarization

Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification

Supervised learning and inference of spiking neural networks with temporal coding

Learning to Represent and Reconstruct 3D Deformable Objects

Language Transformers for Remote Sensing Visual Question Answering

Encoder-Decoder Models for Human Segmentation and Motion Analysis

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Pretraining boosts out-of-domain robustness for pose estimation

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

Self-Supervision By Prediction For Object Discovery In Videos

DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Large-Scale Image Segmentation with Convolutional Networks

Using Deep Learning for Image-Based Plant Disease Detection