Contributions to Label-Efficient Learning in Computer Vision and Remote Sensing

TL;DR

This paper presents multiple novel methods for label-efficient learning in computer vision and remote sensing, addressing challenges like limited annotations and multi-modality, with extensive experiments demonstrating their effectiveness.

Contribution

The paper introduces new techniques for weakly supervised, multi-task, contrastive, and few-shot learning tailored to remote sensing data, advancing label-efficient learning methods.

Findings

Effective anomaly-aware object detection from background images

Improved performance in multi-dataset joint training

Enhanced scene classification with multimodal contrastive learning

Abstract

This manuscript presents a series of my selected contributions to the topic of label-efficient learning in computer vision and remote sensing. The central focus of this research is to develop and adapt methods that can learn effectively from limited or partially annotated data, and can leverage abundant unlabeled data in real-world applications. The contributions span both methodological developments and domain-specific adaptations, in particular addressing challenges unique to Earth observation data such as multi-modality, spatial resolution variability, and scene heterogeneity. The manuscript is organized around four main axes including (1) weakly supervised learning for object discovery and detection based on anomaly-aware representations learned from large amounts of background images; (2) multi-task learning that jointly trains on multiple datasets with disjoint annotations to improve performance on object detection and semantic segmentation; (3) self-supervised and supervised contrastive learning with multimodal data to enhance scene classification in remote sensing; and (4) few-shot learning for hierarchical scene classification using both explicit and implicit modeling of class hierarchies. These contributions are supported by extensive experimental results across natural and remote sensing datasets, reflecting the outcomes of several collaborative research projects. The manuscript concludes by outlining ongoing and future research directions focused on scaling and enhancing label-efficient learning for real-world applications.