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SecONNds: Secure Outsourced Neural Network Inference on ImageNet
Shashank Balla
https://arxiv.org/abs/2506.11586 https://arxiv.org/pd…

SecONNds: Secure Outsourced Neural Network Inference on ImageNet
The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing frameworks suffer from high computational overhead and communication costs, rendering them impractical for real-world deployment. We introduce SecONNds, a non-intrusive secure inference framework optimized for large ImageNet-scale Convolutional Neural Networks. …

Med-URWKV: Pure RWKV With ImageNet Pre-training For Medical Image Segmentation
Zhenhuan Zhou
https://arxiv.org/abs/2506.10858 https://

Med-URWKV: Pure RWKV With ImageNet Pre-training For Medical Image Segmentation
Medical image segmentation is a fundamental and key technology in computer-aided diagnosis and treatment. Previous methods can be broadly classified into three categories: convolutional neural network (CNN) based, Transformer based, and hybrid architectures that combine both. However, each of them has its own limitations, such as restricted receptive fields in CNNs or the computational overhead caused by the quadratic complexity of Transformers. Recently, the Receptance Weighted Key Value (RWKV…

Applying Vision Transformers on Spectral Analysis of Astronomical Objects
Luis Felipe Strano Moraes, Ignacio Becker, Pavlos Protopapas, Guillermo Cabrera-Vives
https://arxiv.org/abs/2506.00294

Applying Vision Transformers on Spectral Analysis of Astronomical Objects
We apply pre-trained Vision Transformers (ViTs), originally developed for image recognition, to the analysis of astronomical spectral data. By converting traditional one-dimensional spectra into two-dimensional image representations, we enable ViTs to capture both local and global spectral features through spatial self-attention. We fine-tune a ViT pretrained on ImageNet using millions of spectra from the SDSS and LAMOST surveys, represented as spectral plots. Our model is evaluated on key task…