Search Results for author:
Found 27 papers, 11 papers with code
Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency
After expanding the training set, we propose a training approach that leverages the specificities and the underlying geometry of this mix of real and synthetic images.
RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation
Class-incremental semantic image segmentation assumes multiple model updates, each enriching the model to segment new categories.
Semantic Image Segmentation: Two Decades of Research
Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image.
SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization
Inspired by properties of semantic segmentation, in this paper we investigate how to leverage robust image segmentation in the context of privacy-preserving visual localization.
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Despite impressive performance for high-level downstream tasks, self-supervised pre-training methods have not yet fully delivered on dense geometric vision tasks such as stereo matching or optical flow.
Ranked #1 on
Optical Flow Estimation
on KITTI 2012
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
More precisely, we propose the pretext task of cross-view completion where the first input image is partially masked, and this masked content has to be reconstructed from the visible content and the second image.
Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark
In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms.
Deep Visual Geo-localization Benchmark
In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architectures, with the flexibility to change individual components of a geo-localization pipeline.
On the Road to Online Adaptation for Semantic Image Segmentation
We propose a new problem formulation and a corresponding evaluation framework to advance research on unsupervised domain adaptation for semantic image segmentation.
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity
While the first provides rich and implicit context for the search, the latter explicitly calls for new traits, or specifies how some elements of the example image should be changed to retrieve the desired target image.
Ranked #11 on
Image Retrieval
on CIRR
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey
Semantic segmentation plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image.
Domain Adaptation in Multi-View Embedding for Cross-Modal Video Retrieval
Given a gallery of uncaptioned video sequences, this paper considers the task of retrieving videos based on their relevance to an unseen text query.
Large-scale Localization Datasets in Crowded Indoor Spaces
In this paper, we introduce 5 new indoor datasets for visual localization in challenging real-world environments.
Deep Visual Domain Adaptation
With recent advances in deep learning models which are extremely data hungry, the interest for visual DA has significantly increased in the last decade and the number of related work in the field exploded.
Benchmarking Image Retrieval for Visual Localization
This paper focuses on understanding the role of image retrieval for multiple visual localization tasks.
Robust Image Retrieval-based Visual Localization using Kapture
To demonstrate this, we present a versatile pipeline for visual localization that facilitates the use of different local and global features, 3D data (e. g. depth maps), non-vision sensor data (e. g. IMU, GPS, WiFi), and various processing algorithms.
Estimating Low-Rank Region Likelihood Maps
Low-rank regions capture geometrically meaningful structures in an image which encompass typical local features such as edges, corners and all kinds of regular, symmetric, often repetitive patterns, that are commonly found in man-made environment.
Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings
We report the first retrieval results on fine-grained actions for the large-scale EPIC dataset, in a generalised zero-shot setting.
R2D2: Repeatable and Reliable Detector and Descriptor
In this work, we argue that salient regions are not necessarily discriminative, and therefore can harm the performance of the description.
From handcrafted to deep local features
This paper presents an overview of the evolution of local features from handcrafted to deep-learning-based methods, followed by a discussion of several benchmarks and papers evaluating such local features.
An Extended Framework for Marginalized Domain Adaptation
First, in order to make the denoised features domain-invariant, we propose a domain regularization that may be either a domain prediction loss or a maximum mean discrepancy between the source and target data.
Domain Adaptation for Visual Applications: A Comprehensive Survey
The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications.
What is the right way to represent document images?
In this article we study the problem of document image representation based on visual features.
Document image classification, with a specific view on applications of patent images
The main focus of this paper is document image classification and retrieval, where we analyze and compare different parameters for the RunLeght Histogram (RL) and Fisher Vector (FV) based image representations.
Incorporating Near-Infrared Information into Semantic Image Segmentation
Based on a state-of-the-art segmentation framework and a novel manually segmented image database (both indoor and outdoor scenes) that contain 4-channel images (RGB+NIR), we study how to best incorporate the specific characteristics of the NIR response.
