3 accepted papers
Weakly-supervised online hashing has garnered significant attention recently, yet several challenges remain unresolved, such as how to effectively denoise tags, and how to efficiently learn hash functions in dynamic online scenarios. To tackle these challenges, we propose a novel method named Tag-Aw…
Due to the high cost of training DNN models, how to protect the intellectual property of DNN models, especially when the models are deployed to users' devices, is becoming an important topic. One practical solution is to use Trusted Execution Environments (TEEs) and researchers have proposed various…
Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focu…