10 accepted papers
In this position paper, we argue that human-centric vision requires skeletal-topology generalization beyond fixed skeletons. Mainstream pose and body pipelines enforce a fixed skeleton graph with an indexed joint list and fixed adjacency, so the fixed joint inventory does not cover structural absenc…
Developing Medical AI relies on large datasets and easily suffers from data scarcity. Generative data augmentation (GDA) using AI generative models offers a solution to synthesize realistic medical images. However, the bias in GDA is often underestimated in medical domains, with concerns about the r
In recent years, diffusion models (DMs) have achieved remarkable success in recommender systems (RSs), owing to their strong capacity to model the complex distributions of item content and user behaviors. Despite their effectiveness, existing methods pose the danger of generating uncredible content…
3D cars are widely used in self-driving systems, virtual and augmented reality, and gaming applications. However, existing 3D car datasets are either synthetic or low-quality, limiting their practical utility and leaving a significant gap with the high-quality real-world 3D car dataset. In this pape…
Self-driving industries usually employ professional artists to build exquisite 3D cars. However, it is expensive to craft large-scale digital assets. Since there are already numerous datasets available that contain a vast number of images of cars, we focus on reconstructing high-quality 3D car model
Federated Learning (FL) is a distributed paradigm aimed at protecting participant data privacy by exchanging model parameters to achieve high-quality model training. However, this distributed nature also makes FL highly vulnerable to backdoor attacks. Notably, the recently proposed state-of-the-art…
Machine unlearning aims to remove the influence of specific samples from a trained model. A key challenge in this process is over-unlearning, where the model's performance on the remaining data significantly drops due to the change in the model's parameters. Existing unlearning algorithms depend on
Machine unlearning aims to enable models to forget specific data instances when receiving deletion requests. Current research centers on efficient unlearning to erase the influence of data from the model and neglects the subsequent impacts on the remaining data. Consequently, existing unlearning alg…
While in-processing fairness approaches show promise in mitigating bias predictions, their potential impact on privacy leakage remains under-explored. We aim to address this gap by assessing the privacy risks of fairness-enhanced binary classifiers with membership inference attacks (MIAs). Surprisin…
GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes. Although some existing detectors work well in detecting clean, known GAN samples, their success is largely attributable to overfitting unstable…