🤖 AI Summary
To address the scarcity of labeled data for event cameras, this paper proposes a cross-modal unsupervised domain adaptation framework that transfers knowledge from a well-annotated frame-based image domain to an unlabeled event-domain. Methodologically, it introduces the first adversarial learning framework integrating self-supervised representation alignment with non-redundant conditional constraints—ensuring both source-target feature alignment and explicit modeling of discriminative modality-specific characteristics between frames and events. The technical pipeline comprises three key components: cross-modal (event-frame) representation learning, adversarial domain adaptation, and non-redundant conditional modeling. Evaluated on two standard event-camera benchmarks, the method achieves significant improvements over existing state-of-the-art approaches, demonstrating superior effectiveness and robustness in cross-modal domain transfer.
📝 Abstract
Event-based cameras provide accurate and high temporal resolution measurements for performing computer vision tasks in challenging scenarios, such as high-dynamic range environments and fast-motion maneuvers. Despite their advantages, utilizing deep learning for event-based vision encounters a significant obstacle due to the scarcity of annotated data caused by the relatively recent emergence of event-based cameras. To overcome this limitation, leveraging the knowledge available from annotated data obtained with conventional frame-based cameras presents an effective solution based on unsupervised domain adaptation. We propose a new algorithm tailored for adapting a deep neural network trained on annotated frame-based data to generalize well on event-based unannotated data. Our approach incorporates uncorrelated conditioning and self-supervised learning in an adversarial learning scheme to close the gap between the two source and target domains. By applying self-supervised learning, the algorithm learns to align the representations of event-based data with those from frame-based camera data, thereby facilitating knowledge transfer.Furthermore, the inclusion of uncorrelated conditioning ensures that the adapted model effectively distinguishes between event-based and conventional data, enhancing its ability to classify event-based images accurately.Through empirical experimentation and evaluation, we demonstrate that our algorithm surpasses existing approaches designed for the same purpose using two benchmarks. The superior performance of our solution is attributed to its ability to effectively utilize annotated data from frame-based cameras and transfer the acquired knowledge to the event-based vision domain.