15 accepted papers
Nuclei instance segmentation in histopathology images is essential for diagnostic accuracy and downstream computational tasks, yet this task relies heavily on expensive pixel level annotations. Although point level annotations substantially reduce the annotation burden for pathologists, many existin
Vision Transformers (ViTs) exhibit notable susceptibility to adversarial attacks, presenting a significant challenge for their deployment in security-sensitive applications. Despite their considerable empirical successes, a rigorous theoretical foundation for ViT's adversarial generalization behavio
Partial linear models (PLM) have attracted much attention for regression estimation and variable selection due to their feasibility on utilizing linear and nonlinear approximations jointly. However, theoretical understanding of how they control the false discovery rate (FDR) during variable selectio
The vulnerability of deep neural networks to adversarial examples poses significant challenges to their reliable deployment. Among existing empirical defenses, adversarial training and robust distillation have proven the most effective. In this paper, we identify a property originally associated wit…
Federated Adversarial Learning (FAL) enhances model robustness by integrating adversarial training into the federated learning framework. Despite recent advances proposing efficient FAL algorithms, existing work has mainly focused on convergence properties, with limited understanding of their genera…
Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous research often relied on single data sources, missing the opportunity to combine var…
Recent advances in deep generative models have made it easier to manipulate face videos, raising significant concerns about their potential misuse for fraud and misinformation. Existing detectors often perform well in in-domain scenarios but fail to generalize across diverse manipulation techniques…
In this paper, we propose FakeRadar, a novel deepfake video detection framework designed to address the challenges of cross-domain generalization in real-world scenarios. Existing detection methods typically rely on manipulation-specific cues, performing well on known forgery types but exhibiting se…
As deep learning techniques advance rapidly, deepfake speech synthesized through text-to-speech or voice conversion networks is becoming increasingly realistic, posing significant challenges for detection and raising potential threats to social security. This growing realism has prompted extensive r…
Recent advancements in text-to-speech and speech conversion technologies have enabled the creation of highly convincing synthetic speech. While these innovations offer numerous practical benefits, they also cause significant security challenges when maliciously misused. Therefore, there is an urgent…
Image-level weakly supervised semantic segmentation (WSSS) reduces the dependence on high-quality data annotation, which plays a crucial role in computational pathology. Benefit from the ability to localize the objects with only binary labels, Class Activation Map (CAM) is a widely used method to in…
Nuclei segmentation is a fundamental and critical step in digital pathological image analysis. Fully supervised nuclei segmentation requires a lot of pixel-by-pixel manual annotation by pathologists, which is very time-consuming and laborious. To minimize the labeling burden of pathologists, this pa…
Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always be…
With the growing applicability of multi-view multi-person 3D human pose estimation across diverse scenarios, the impact of external environmental factors and occlusion on accuracy has garnered substantial attention. In this research, we introduce a novel approach to multi-view multi-person 3D human…
This paper proposes the pairwise linear regression classification (PLRC) for image set retrieval. In PLRC, we first define a new concept of the unrelated subspace and introduce two strategies to constitute the unrelated subspace. In order to increase the information of maximizing the query set and t…