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Found 9 papers, 1 papers with code
BiPer: Binary Neural Networks using a Periodic Function
In contrast to current BNN approaches, we propose to employ a binary periodic (BiPer) function during binarization.
Privacy-preserving Optics for Enhancing Protection in Face De-identification
Specifically, our approach first learns an optical encoder along with a regression model to obtain a face heatmap while hiding the face identity from the source image.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
Learning Semantic Segmentation with Query Points Supervision on Aerial Images
In this work, we present a weakly supervised learning algorithm to train semantic segmentation algorithms that only rely on query point annotations instead of full mask labels.
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
This paper introduces a new data augmentation architecture that generates synthetic multiparametric (T1 arterial, T1 portal, and T2) magnetic resonance images (MRI) of massive macrotrabecular subtype hepatocellular carcinoma with their corresponding tumor masks through a generative deep learning approach.
PrivHAR: Recognizing Human Actions From Privacy-preserving Lens
The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition.
A fast and Accurate Similarity-constrained Subspace Clustering Framework for Unsupervised Hyperspectral Image Classification
Accurate land cover segmentation of spectral images is challenging and has drawn widespread attention in remote sensing due to its inherent complexity.
Learning Privacy-Preserving Optics for Human Pose Estimation
However, we also want the camera to capture useful information to perform computer vision tasks.
Spatial Sparse subspace clustering for Compressive Spectral imaging
This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements.
