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

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Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

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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 ...

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 ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

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Estimating 3D human poses from 2D images is challenging due to occlusions and projective acquisition. Learning-based approaches have been largely studied to address this challenge, both in single and multi-view setups. These solutions however fail to gener ...

Tightly-coupled sensor orientation, i.e. the simultaneous processing of temporal (GNSS and raw inertial) and spatial (image and lidar) constraints in a common adjustment, has demonstrated significant improvement in the quality of attitude determination wit ...

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The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between syntheti ...

Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor ...

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 ...

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

Object detection plays a critical role in various computer vision applications, encompassing domains like autonomous vehicles, object tracking, and scene understanding. These applica- tions rely on detectors that generate bounding boxes around known object ...

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, ...

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

Chattez avec Graph Search

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

The multimodality cell segmentation challenge: toward universal solutions

Aggregating Spatial and Photometric Context for Photometric Stereo

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Driving and suppressing the human language network using large language models

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Toward Automatic Typography Analysis: Serif Classification and Font Similarities

MPL: Lifting 3D Human Pose from Multi-view 2D Poses

Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment

Random matrix methods for high-dimensional machine learning models

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

Robust machine learning for neuroscientific inference

Learning Transformations To Reduce the Geometric Shift in Object Detection

Rigidity-Aware Detection for 6D Object Pose Estimation

Transformer Models for Vision

Detecting Anomalies and Obstacles in Road Scenes

Improving Object Detection under Domain Shifts

Interpretable Representation Learning and Evaluation for Abstractive Summarization