Deep learning has shown remarkable success in medical image classification; however, model performance remains highly sensitive to data imbalance, annotation uncertainty, and subtle inter-class variability. Conventional loss functions such as cross-entropy often fail to capture these challenges, resulting in overconfident or poorly calibrated predictions. To address this, we propose the Probabilistic Margin-Aware Focal Loss (PMAF Loss), a unified objective designed to enhance discriminability, robustness, and reliability in medical image classification. PMAF integrates three key mechanisms: focal modulation to emphasize hard and informative samples, uncertainty weighting to adaptively suppress noisy or ambiguous predictions, and margin-based feature regularization to enforce interclass separation in the embedding space. Extensive experiments on multiple benchmark medical datasets demonstrate that our method consistently outperforms standard crossentropy loss in terms of accuracy, calibration, and interpretability.
This paper reviews the Robust Thermal-Image Object Detection (RTIOD) Challenge, organized as part of the 6th Real-World Surveillance (RWS) Workshop at the Winter Conference on Applications of Computer Vision (WACV) 2026. The challenge aims to stimulate the development and rigorous evaluation of object detectors that remain reliable under long-term appearance drift in real-world surveillance settings. To this end, RTIOD is built on the Long-term Thermal Drift v2 dataset and evaluates submissions using both global and month-wise metrics (e.g., mAP@0.5), enabling direct assessment of temporal stability over extended periods. In total, 60 participants registered and 14 teams submitted predictions for the final test phase. Beyond reporting the leaderboard and summarizing competing approaches, this paper provides an analysis of why top methods succeed. We distill recurring design patterns (e.g., thermal representation choices, data curation strategies, and deployment-informed priors), characterize month-dependent failure modes that reveal seasonal drift effects, and examine dominant error sources (notably localization and classification) that limit performance. The resulting insights offer practical guidance for building more temporally consistent thermal detectors and establish reference baselines and benchmarks for future research in robust thermalimage object detection.
Text-to-image generation has demonstrated impressive performance in various applications, thanks to the powerful generative models. However, the unleashed creativity of such models can lead to the abuse of undesired generative content, which undermines the social impact and sustainability of generative AI. Machine unlearning is proposed to eliminate specific content from AI models and can be used to erase key value representations associated with undesired prompts. Yet, complete removal of a concept is non-trivial due to semantic complexity, leading to both adversarial recovery and image quality degradation. On this basis, this paper introduces a Robust Erasing Method in Diffusion models (REMinD) to unlearn target concepts flexibly, which can handle hypernym concepts using various hyponym prompts. To enhance unlearning and image quality, a multi-level loss function is designed to explore local, adjacent, and global representations, removing target unlearning concepts while preserving generation quality for the remaining global concepts. Extensive experiments on four unlearning tasks demonstrate that the proposed method achieves more effective and robust unlearning, e.g., reducing the attack success rate by up to 28% against state-of-the-art style attacks.
The detection of foreign object debris (FOD) on railway transmission lines and airport runways is essential for maintaining operational safety and minimizing risks. However, accurately identifying small, occluded, or cluttered objects remains a significant challenge for existing object detection models due to limitations in feature representation and high computational overhead. This work introduces YOLO-OSA, a lightweight object detector enhanced with an Optimized ShuffleAttention (OSA) mechanism integrated into both the backbone and neck. The OSA module selectively emphasizes informative spatial and channel features, enabling improved multi-scale feature aggregation while reducing computational complexity. The model achieves high detection accuracy with fewer parameters and lower FLOPs, making it suitable for deployment on resourceconstrained edge devices. Experiments on the RailFOD23 and FOD-A datasets show that YOLO-OSA outperforms state-of-the-art models, particularly in detecting small and occluded FOD instances. On the MS COCO benchmark, YOLO-OSA achieves a 29.2% improvement in mAP@50-95 over YOLOv8n and a 7.35% gain over YOLOv8s, while reducing FLOPs by 3.45% and 70.6%, respectively. These results highlight the effectiveness and generalization capability of YOLO-OSA for real-time, safety-critical object detection applications.
