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TransIFF: An Instance-Level Feature Fusion Framework for Vehicle-Infrastructure Cooperative 3D Detection with Transformers
Furthermore, it can align the domain gaps between vehicle and infrastructure features, and improve the robustness of feature fusion, leading to a high cooperative perception accuracy.
Spectrum of non-Hermitian deep-Hebbian neural networks
Neural networks with recurrent asymmetric couplings are important to understand how episodic memories are encoded in the brain.
SideRT: A Real-time Pure Transformer Architecture for Single Image Depth Estimation
To the best of our knowledge, this is the first work to show that transformer-based networks can attain state-of-the-art performance in real-time in the single image depth estimation field.
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