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EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition


Core Concepts
Proposing the EventDance framework for unsupervised cross-modal adaptation in event-based object recognition.
Abstract
Introduces the novel problem of cross-modal adaptation from images to events without access to labeled source data. Proposes the EventDance framework consisting of RMB and MKA modules for knowledge transfer. Demonstrates superior performance compared to prior methods on three benchmark datasets. Ablation studies confirm the effectiveness of different components and event representations. Extension experiment shows flexibility in adapting from edge maps to event voxel grids.
Stats
"Experiments on three benchmark datasets with two adaption settings show that EventDance is on par with prior methods utilizing the source data." "The experimental results demonstrate that our EventDance significantly outperforms the prior source-free domain adaptation methods e.g., [33], in addressing the challenging cross-modal task."
Quotes
"In this paper, we make the first attempt at achieving the cross-modal (i.e., image-to-events) adaptation for event-based object recognition without accessing any labeled source image data owning to privacy and commercial issues." "Event cameras, a.k.a., the silicon retina, are bio-inspired novel sensors that perceive per-pixel intensity changes asynchronously and produce event streams encoding the time, pixel position, and polarity of the intensity changes."

Key Insights Distilled From

by Xu Zheng,Lin... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14082.pdf
EventDance

Deeper Inquiries

How can EventDance be extended to other downstream tasks beyond object recognition?

EventDance can be extended to other downstream tasks by adapting the framework to suit the specific requirements of different tasks. For instance, for action recognition, the event streams could be processed and transformed into representations that capture temporal dynamics effectively. This may involve incorporating recurrent neural networks or attention mechanisms to handle sequential data efficiently. Additionally, for scene segmentation tasks, the surrogate image domain created in EventDance could be leveraged to generate pixel-wise annotations from events using techniques like semantic segmentation networks.

What counterarguments exist against using surrogate data in training for knowledge transfer?

One potential counterargument against using surrogate data in training is related to the fidelity and representativeness of the generated images compared to real source modality data. The quality of the reconstructed images may not fully capture all nuances present in actual images, leading to potential information loss or distortion during knowledge transfer. Moreover, there might be concerns about introducing biases or inaccuracies through the reconstruction process that could impact model performance negatively.

How might advancements in event camera technology impact future developments in cross-modal adaptation?

Advancements in event camera technology are likely to have a significant impact on future developments in cross-modal adaptation by providing more sophisticated and high-resolution event streams. Higher resolution event cameras would enable capturing finer details and richer spatio-temporal information from scenes, enhancing the quality of input data for cross-modal models. This improved data quality can lead to better feature extraction and representation learning across modalities, ultimately boosting performance in various cross-modal tasks such as object recognition, action detection, and scene understanding. Additionally, enhanced sensor capabilities may facilitate more seamless integration with deep learning architectures designed for efficient processing of asynchronous events.
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