RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization

We study an important, yet largely unexplored problem of large-scale cross-modal visual localization by matching ground RGB images to a geo-referenced aerial LIDAR 3D point cloud (rendered as depth images). Prior works were demonstrated on small datasets and did not lend themselves to scaling up for large-scale applications. To enable large-scale evaluation, we introduce a new dataset containing over 550K pairs (covering 143 km^2 area) of RGB and aerial LIDAR depth images. We propose a novel joint embedding based method that effectively combines the appearance and semantic cues from both modalities to handle drastic cross-modal variations. Experiments on the proposed dataset show that our model achieves a strong result of a median rank of 5 in matching across a large test set of 50K location pairs collected from a 14km^2 area. This represents a significant advancement over prior works in performance and scale. We conclude with qualitative results to highlight the challenging nature of this task and the benefits of the proposed model. Our work provides a foundation for further research in cross-modal visual localization.Comment: ACM Multimedia 202

Cross-View Visual Geo-Localization for Outdoor Augmented Reality

Precise estimation of global orientation and location is critical to ensure a compelling outdoor Augmented Reality (AR) experience. We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. Recently, neural network-based methods have shown state-of-the-art performance in cross-view matching. However, most of the prior works focus only on location estimation, ignoring orientation, which cannot meet the requirements in outdoor AR applications. We propose a new transformer neural network-based model and a modified triplet ranking loss for joint location and orientation estimation. Experiments on several benchmark cross-view geo-localization datasets show that our model achieves state-of-the-art performance. Furthermore, we present an approach to extend the single image query-based geo-localization approach by utilizing temporal information from a navigation pipeline for robust continuous geo-localization. Experimentation on several large-scale real-world video sequences demonstrates that our approach enables high-precision and stable AR insertion.Comment: IEEE VR 202

Diachronic cross-modal embeddings

This work has been partially funded by the CMU Portugal research project GoLocal Ref. CMUP-ERI/TIC/0046/2014, by the H2020 ICT project COGNITUS with the grant agreement no 687605 and by the FCT project NOVA LINCS Ref. UID/CEC/04516/2019. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.Understanding the semantic shifts of multimodal information is only possible with models that capture cross-modal interactions over time. Under this paradigm, a new embedding is needed that structures visual-textual interactions according to the temporal dimension, thus, preserving data's original temporal organisation. This paper introduces a novel diachronic cross-modal embedding (DCM), where cross-modal correlations are represented in embedding space, throughout the temporal dimension, preserving semantic similarity at each instant t. To achieve this, we trained a neural cross-modal architecture, under a novel ranking loss strategy, that for each multimodal instance, enforces neighbour instances' temporal alignment, through subspace structuring constraints based on a temporal alignment window. Experimental results show that our DCM embedding successfully organises instances over time. Quantitative experiments, confirm that DCM is able to preserve semantic cross-modal correlations at each instant t while also providing better alignment capabilities. Qualitative experiments unveil new ways to browse multimodal content and hint that multimodal understanding tasks can benefit from this new embedding.publishersversionpublishe

Learning Robust Visual-Semantic Retrieval Models with Limited Supervision

In recent years, tremendous success has been achieved in many computer vision tasks using deep learning models trained on large hand-labeled image datasets. In many applications, this may be impractical or infeasible, either because of the non-availability of large datasets or the amount of time and resource needed for labeling. In this respect, an increasingly important problem in the field of computer vision, multimedia and machine learning is how to learn useful models for tasks where labeled data is sparse. In this thesis, we focus on learning comprehensive joint representations for different cross-modal visual-textual retrieval tasks leveraging weak supervision, that is noisier and/or less precise but cheaper and/or more efficient to collect. Cross-modal visual-textual retrieval has gained considerable momentum in recent years due to the promise of deep neural network models in learning robust aligned representations across modalities. However, the difficulty in collecting aligned pairs of visual data and natural language description and limited availability such pairs in existing datasets makes it extremely difficult to train effective models, which would generalize well to uncontrolled scenarios as they are heavily reliant on large volumes of training data that closely mimic what is expected in the test cases. In this regard, we first present our work on developing a multi-faceted joint embedding framework-based video to text retrieval system that utilizes multi-modal cues (e.g., objects, action, place, sound) from videos to reduce the effect of limited data. Then, we describe our approach on training text to video moment retrieval systems leveraging only video-level text descriptions without any temporal boundary annotations. Next, we present our work on learning powerful joint representations of images and text from small fully annotated datasets with supervision from weakly-annotated web images. Extensive experimentation on different benchmark datasets demonstrates that our approaches show substantially better performance compared to baselines and state-of-the-art alternative approaches