Traditional object detection models face significant limitations in supporting the detection of real-time (RT) infrared substation equipment. Unlike visible images, infrared images exhibit sparse texture features and complex, noisy backgrounds, hindering effective feature extraction. Furthermore, detection accuracy is often correlated with the size of the model, which poses challenges for deployment on resource-constrained devices. To address these challenges, a novel detection model named real-time-infrared context aggregation (RT-IRCA) is proposed. The model comprises two key components. 1) an IRCA module that captures richer contextual information from infrared images to enhance feature extraction, and 2) a multilevel knowledge distillation strategy that reduces model parameters and balances detection accuracy with computational complexity. Furthermore, to mitigate the scarcity of infrared image datasets, a dedicated dataset named the infrared substation equipment dataset is constructed. Extensive experiments on multiple datasets demonstrate that RT-IRCA achieves significant improvements in detection performance, while maintaining high robustness and efficiency, making it well suited for RT applications.

Medical image segmentation remains challenging due to the high cost of pixel-level annotations for training. In the context of weak supervision, clinician gaze data captures regions of diagnostic interest; however, its sparsity limits its use for segmentation. In contrast, vision-language models (VLMs) provide semantic context through textual descriptions but lack the explanation precision required. Recognizing that neither source alone suffices, we propose a teacher-student framework that integrates both gaze and language supervision, leveraging their complementary strengths. Our key insight is that gaze data indicates "where" clinicians focus during diagnosis, while VLMs explain "why" those regions are significant. To implement this, the teacher model first learns from gaze points enhanced by VLM-generated descriptions of lesion morphology, establishing a foundation for guiding the student model. The teacher then directs the student through three strategies: 1) Multi-scale feature alignment to fuse visual cues with textual semantics; 2) Confidence-weighted consistency constraints to focus on reliable predictions; 3) Adaptive masking to limit error propagation in uncertain areas. Experiments on the Kvasir-SEG, NCI-ISBI, and ISIC datasets show that our method achieves Dice scores of 80.78%, 80.53%, and 84.22%, respectively-improving 3-5% over gaze baselines without increasing the annotation burden. By preserving correlations among predictions, gaze data, and lesion descriptions, our framework also maintains clinical interpretability. This work illustrates how integrating human visual attention with AI-generated semantic context can effectively overcome the limitations of individual weak supervision signals, thereby advancing the development of deployable, annotation-efficient medical AI systems. Code is available at: https://github.com/jingkunchen/FGI.

Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Yueming Jin, Vicente Grau. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Deep neural networks have demonstrated remarkable performance across numerous learning tasks but often suffer from miscalibration, resulting in unreliable probability outputs. This has inspired many recent works on mitigating miscalibration, particularly through post-hoc recalibration methods that aim to obtain calibrated probabilities without sacrificing the classification performance of pre-trained models. In this study, we summarize and categorize previous works into three general strategies: intuitively designed methods, binning-based methods, and methods based on formulations of ideal calibration. Through theoretical and practical analysis, we highlight ten common limitations in previous approaches. To address these limitations, we propose a probabilistic learning framework for calibration called $h$h-calibration, which theoretically constructs an equivalent learning formulation for canonical calibration with boundedness. On this basis, we design a simple yet effective post-hoc calibration algorithm. Our method not only overcomes the ten identified limitations but also achieves markedly better performance than traditional methods, as validated by extensive experiments. We further analyze, both theoretically and experimentally, the relationship and advantages of our learning objective compared to traditional proper scoring rule. In summary, our probabilistic framework derives an approximately equivalent differentiable objective for learning error-bounded calibrated probabilities, elucidating the correspondence and convergence properties of computational statistics with respect to theoretical bounds in canonical calibration. The theoretical effectiveness is verified on standard post-hoc calibration benchmarks by achieving state-of-the-art performance. This research offers valuable reference for learning reliable likelihood in related fields.

In recent years, there has been a notable surge in the adoption of weakly-supervised learning for medical image segmentation, utilizing scribble annotation as a means to potentially reduce annotation costs. However, the inherent characteristics of scribble labeling, marked by incompleteness, subjectivity, and a lack of standardization, introduce inconsistencies into the annotations. These inconsistencies become significant challenges for the network's learning process, ultimately affecting the performance of segmentation. To address this challenge, we propose creating a reference set to guide pixel-level feature matching, constructed from class-specific tokens and pixel-level features extracted from variously images. Serving as a repository showcasing diverse pixel styles and classes, the reference set becomes the cornerstone for a pixel-level feature matching strategy. This strategy enables the effective comparison of unlabeled pixels, offering guidance, particularly in learning scenarios characterized by inconsistent and incomplete scribbles. The proposed strategy incorporates smoothing and regression techniques to align pixel-level features across different images. By leveraging the diversity of pixel sources, our matching approach enhances the network's ability to learn consistent patterns from the reference set. This, in turn, mitigates the impact of inconsistent and incomplete labeling, resulting in improved segmentation outcomes. Extensive experiments conducted on three publicly available datasets demonstrate the superiority of our approach over state-of-the-art methods in terms of segmentation accuracy and stability. The code will be made publicly available at https://github.com/jingkunchen/scribble-medical-segmentation.

Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of manual annotation of clinicians by using unlabelled data, when developing medical image segmentation tools. However, to date, most existing semi-supervised learning (SSL) algorithms treat the labelled images and unlabelled images separately and ignore the explicit connection between them; this disregards essential shared information and thus hinders further performance improvements. To mine the shared information between the labelled and unlabelled images, we introduce a class-specific representation extraction approach, in which a task-affinity module is specifically designed for representation extraction. We further cast the representation into two different views of feature maps; one is focusing on low-level context, while the other concentrates on structural information. The two views of feature maps are incorporated into the task-affinity module, which then extracts the class-specific representations to aid the knowledge transfer from the labelled images to the unlabelled images. In particular, a task-affinity consistency loss between the labelled images and unlabelled images based on the multi-scale class-specific representations is formulated, leading to a significant performance improvement. Experimental results on three datasets show that our method consistently outperforms existing state-of-the-art methods. Our findings highlight the potential of consistency between class-specific knowledge for semi-supervised medical image segmentation. The code and models are to be made publicly available at https://github.com/jingkunchen/TAC.

This paper investigates the problem of position optimization for multiple mobile relays. The authors propose a new scheme involving multiple mobile relays with fixed orbits to avoid collision risk among the multiple mobile relays and to relax the requirements of the existing approaches, e.g., multiple on‐board antennas and complex feedback signals. The proposed scheme exhibits satisfactory performance with low cost and low complexity, as it requires only a single on‐board antenna and merely a one‐bit feedback signal, which is highly suitable for a scenario in which the source node has limited energy at its disposal, such as in a disaster area. The authors further design three implementation algorithms based on the easy implementation of the proposed scheme. The first algorithm is the simplest one in so far as it utilizes only single positive feedback information, but it exhibits poor performance. The second algorithm achieves better performance by exploiting successive points of negative feedback information. Based on the second algorithm, the third algorithm exhibits the best performance by further exploiting cumulative points of positive feedback information.