Adversarial distillation (AD) aims to mitigate deep neural networks' inherent vulnerability to adversarial attacks, thereby providing robust protection for compact models through teacher-student interactions. Despite advancements, existing AD studies still suffer from insufficient robustness due to the limitations of fixed attack strength and attention region shifts. To address these challenges, we propose a strength-adaptive Info-maximizing Adversarial Robustness Distillation paradigm, namely "InfoARD", which strategically incorporates the Attack-Strength Adaptation (ASA) and Mutual-Information Maximization (MIM) to enhance adversarial robustness against adversarial attacks and perturbations. Unlike previous adversarial training (AT) methods that utilize fixed attack strength, the ASA mechanism is designed to capture smoother and generalized classification boundaries by dynamically tailoring the attack strength based on the characteristics of individual instances. Benefiting from mutual information constraints, our MIM strategy ensures the student model effectively learns from various levels of feature representations and attention patterns, thereby deepening the student model's understanding of the teacher model's decision-making processes. Furthermore, a comprehensive multi-granularity distillation is conducted to capture knowledge across multiple dimensions, enabling a more effective transfer of knowledge from the teacher model to the student model. Note that our InfoARD can be seamlessly integrated into existing AD frameworks, further boosting the adversarial robustness of deep learning models. Extensive experiments on various challenging datasets consistently demonstrate the effectiveness and robustness of our InfoARD, surpassing previous state-of-the-art methods.

In clinical practice, panoramic dental radiography is a widely employed imaging technique that can provide a detailed and comprehensive view of dental structures and surrounding tissues for identifying various oral anomalies. However, due to the complexity of oral anomalies and the scarcity of available data, existing research still suffers from substantial challenges in automated oral anomaly detection. To this end, this paper presents a new hospital-scale panoramic X-ray benchmark, namely "OralXrays-91", which consists of 12,688 panoramic X-ray images with 84,113 meticulously annotated instances across nine common oral anomalies. Correspondingly, we propose a personalized Multi-Object Query-Aware Mining (MOQAM) paradigm, which jointly incorporates the Distribution-IoU Region Proposal Network (DI-RPN) and Class-Balanced Spherical Contrastive Regularization (CB-SCR) mechanisms to address the challenges posed by multi-scale variations and class-imbalanced distributions. To the best of our knowledge, this is the first attempt to develop AI-driven diagnostic systems specifically designed for multi-object oral anomaly detection, utilizing publicly available data resources. Extensive experiments on the newly-published OralXrays-9 dataset and real-world nature scenarios consistently demonstrate the superiority of our MOQAM in revolutionizing oral healthcare practices.

Class-incremental learning (CIL) enables models to learn new tasks without forgetting previously acquired knowledge. However, existing CIL approaches often struggle with inadequate adaptation to task-specific feature spaces and catastrophic forgetting of previously-acquired knowledge, compromising the models’ plasticity and stability. To address these challenges, this paper proposes a novel differential optimization paradigm called DO-CIL, which incorporates task-agnostic slow learner (TSL) with task-specific fast adapter (TFA) for rehearsal-free CIL. Specifically, TSL aims to effectively capture shared knowledge with low learning rates for robust generalization, while TFA allows pre-trained models to adapt to new task-specific feature spaces. Benefitting from the classifier retraining strategy, a learnable semantic shift network is also proposed to align prototypes with the evolving model representation, facilitating the retraining of task-specific classifiers based on these updated prototypes. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority and effectiveness of our DO-CIL approach compared to state-of-the-art baselines.

Despite advancements in Transformer-based detectors for small object detection (SOD), recent studies show that these detectors still face challenges due to inherent noise sensitivity in feature pyramid networks (FPN) and diminished query quality in existing label assignment strategies. In this paper, we propose a novel Noise-Resilient Query Optimization (NRQO) paradigm, which innovatively incorporates the Noise-Tolerance Feature Pyramid Network (NT-FPN) and the Pairwise-Similarity Region Proposal Network (PS-RPN). Specifically, NTFPN mitigates noise during feature fusion in FPN by preserving spatial and semantic information integrity. Unlike existing label assignment strategies, PS-RPN generates a sufficient number of high-quality positive queries by enhancing anchor-ground truth matching through position and shape similarities, without the need for additional hyperparameters. Extensive experiments on multiple benchmarks consistently demonstrate the superiority of NRQO over state-of-the-art baselines.