Language Models (LMs) have advanced diagnosis prediction by leveraging the semantic understanding of medical concepts in Electronic Health Records (EHRs). Despite these advancements, existing LM-based methods often fail to capture the structures of medical concepts (e.g., hierarchy structure from domain knowledge). In this paper, we propose BoxLM, a novel framework that unifies the structures and semantics of medical concepts for diagnosis prediction. Specifically, we propose a structure-semantic fusion mechanism via box embeddings, which integrates both ontology-driven and EHR-driven hierarchical structures with LM-based semantic embeddings, enabling interpretable medical concept representations. Furthermore, in the box-aware diagnosis prediction module, an evolve-and-memorize patient box learning mechanism is proposed to model the temporal dynamics of patient visits, and a volume-based similarity measurement is proposed to enable accurate diagnosis prediction. Extensive experiments demonstrate that BoxLM consistently outperforms state-of-the-art baselines, especially achieving strong performance in few-shot learning scenarios, showcasing its practical utility in real-world clinical settings.

Vehicle detection from surveillance videos is of great significance for various Intelligent Transportation System (ITS) applications. However, existing deep learning methods oftentimes fail under nighttime conditions on account of the lack of sufficient labeled nighttime data. To fill this gap, this paper proposes a novel framework for all-day vehicle detection, by introducing an illumination-adjustable GAN (IA-GAN). The IA-GAN transforms labeled daytime images into multiple nighttime images with diverse illumination, using an adjustable illumination vector as input. Notably, we utilize gray histogram distributions to automatically generate illumination labels, by which IA-GAN gains the knowledge of simulating lights. Following that, we construct a large dataset containing both labeled daytime images and all generated synthetic nighttime images with bounding box labels. Finally, a detector named Day-Night Balanced EfficientDet (DNBED) is developed for all-day vehicle detection. The experiments show that the proposed framework yields promising performance for all-day vehicle detection and competitive results for nighttime vehicle detection compared to existing GANs, indicating the effectiveness of proposed framework. The privacy-sanitized image data and its corresponding labels will be made publicly available at https://github.com/vvgoder/SEU_PML_Dataset.

With the rapid growth of multimedia-sharing platforms (e.g. Twitter and TikTok), multimedia recommender systems have become fundamental for helping users alleviate information overload and discover items of interest. Existing multimedia recommendation methods often incorporate various auxiliary modalities (e.g., visual, textual, and acoustic) to describe item characteristics and improve task performance. However, these methods usually assume that each item is associated with complete modalities, ignoring the prevalence of missing modality issues in real-world scenarios. To deal with the challenge of missing modalities, in this paper, we propose a novel framework of Contrastive Intra- and Inter-Modality Generation (CI2MG) for enhancing incomplete multimedia recommendation. We first develop a contrastive intra- and inter-modality generation module for the missing modalities, where the intra-modality representation is updated through clustering-based hypergraph convolution and inter-modality representation is obtained by optimal transport between different modalities. To tackle the challenge of insufficient and incomplete supervision labels during intra- and inter-modality generation, a modality-aware contrastive learning paradigm is introduced based on an augmentation between the intra-modality view and inter-modality view. Furthermore, to learn task-related representations from the generative modalities and further improve the performance of recommendation, we design an enhanced multimedia recommendation module to alleviate the influences driven by task-irrelevant noise. Extensive experiments on real-world datasets show the superiority of our proposed CI2MG framework in offering great potential for personalized multimedia recommendation over the state-of-the-art baselines regarding Recall, NDCG, and Precision metrics.

Crash detection is of great significance for traffic emergency management. Video-based approaches can effectively save the manpower monitoring cost and have achieved promising results in recent studies. However, they sometimes fail to correctly identify crashes in congested traffic. To fill the gap, this paper proposes a novel appearance-motion network for improving video-based crash detection performance in congested traffic. The appearance-motion network utilizes two paralleled convolutional networks (i.e., an appearance network and a motion network) to extract both appearance features and motion features of crashes. To learn discriminative appearance features for differentiating crashes in congested traffic scene (CCT) with non-crashes in congested traffic scene (NCCT), an auxiliary network combined with a triplet loss are introduced to train the appearance network. To better capture crash motion features in congested traffic, an optical flow learner is built in the motion network and trained to extract more fine-grained motion information. Moreover, a temporal attention module is applied to enable the motion network to focus on valuable frames. Experimental results show that the proposed network achieves a state-of-the-art result on crash detection and the introduction of the three components (i.e., the auxiliary network, the optical flow learner and the temporal attention module) effectively reduces false alarm rate by 28.07% and miss rate by 27.08% on crash detection in congested traffic. Our dataset will be available athttps://github.com/vvgoder/Dataset_for_crashdetection.

Traffic equipment detection from surveillance videos is of practical significance for temporary traffic element update in high-precision maps. However, there is little relative research developed due to limited labeled data. Based on a detector dubbed Faster R-CNN, we propose an automated learning framework that utilizes easy-to-obtain Internet images containing traffic equipment to acquire the capability of detecting traffic equipment from surveillance videos. In this framework, an appearance weighting module using a comprehensive feature aggregation method is designed to allow Faster R-CNN to converge and generalize quickly by taking limited data (i.e., less than 30 images per class) as input. To further address the cross-domain issue brought by the domain gap between the Internet images and the surveillance video frames, a domain adaptation learning scheme is developed, which aims to align the two domains and guide the framework to learn more robust domain-invariant features. Experimental results show that both the appearance weighting module and the domain adaptation learning scheme could bring a great performance improvement. Moreover, the combination of the two results in a state-of-the-art performance (mAP of 44.6%) even if only 30 training images per class are provided. To sum up, the proposed framework is suitable for traffic equipment detection from surveillance videos and provides an inspiration for other detection tasks with limited and cross-domain data, allowing humans to reduce their efforts and time required for arduous data collection and annotation.