Reliable navigation of autonomous vessels critically depends on robust situational awareness, particularly object detection. For this, an accurate, 360-degree perception of the surrounding environment is essential. However, most existing datasets lack the comprehensive multi-view data required for this full environmental coverage. This absence of large-scale, multi-view image datasets specifically designed for maritime situational awareness on vessels presents a significant challenge. To address this, we introduce the Multi-View Maritime Vision (MV2) dataset, comprising 159,386 visible-light images captured from six distinct viewpoints around a vessel. MV2 provides a complete 360-degree omnidirectional perspective, offering critical support for maritime situational awareness applications. The dataset includes object bounding boxes, along with semantic, instance, and panoptic segmentation labels, and encompasses a wide range of environmental conditions, supporting diverse computer-vision tasks. Additionally, we benchmarked state-of-the-art object-detection and panoptic-segmentation models on MV2, demonstrating its contribution to advancing maritime autonomy research. The dataset is available at https://sites.google.com/view/multi-view-maritime-vision.

This study addresses the challenge of robust object detection in maritime environments, where dynamic conditions such as fog, brightness variations, and motion blur can degrade accuracy. We propose a novel framework, Joint Semantic Learning (JSL), which combines ocean scene segmentation and object detection to improve both performance and robustness. JSL incorporates the ocean scene segmentation module into the detection network during training and removes it during inference, ensuring no additional computational overhead. Through ocean scene segmentation, the feature extractor learns to understand the overall context of the image and extract detailed information about objects. Extensive experiments show that JSL, applied to various convolutional neural network-based detectors, achieves significant performance improvements on maritime datasets SMD and SeaShips. Notably, the proposed method shows substantial performance gains on the SMD-C and SeaShips-C datasets, which include adverse conditions, demonstrating the robustness of the proposed method. Furthermore, experiments comparing our method with existing state-of-the-art multi-task methods on the Cityscapes dataset validate its effectiveness in generalizing to urban environments. The efficient integration of spatial and semantic information of JSL ensures accurate and reliable object detection across diverse applications. Our code is available at: https://github.com/gistailab/JSL.

Abstract Multi-agent trajectory prediction is crucial to autonomous driving and understanding the surrounding environment. Learning-based approaches for multi-agent trajectory prediction, such as primarily relying on graph neural networks, graph transformers, and hypergraph neural networks, have demonstrated outstanding performance on real-world datasets in recent years. However, the hypergraph transformer-based method for trajectory prediction is yet to be explored. Therefore, we present a M ultisc A le R elational T ransformer ( MART ) network for multi-agent trajectory prediction. MART is a hypergraph transformer architecture to consider individual and group behaviors in transformer machinery. The core module of MART is the encoder, which comprises a Pair-wise Relational Transformer (PRT) and a Hyper Relational Transformer (HRT). The encoder extends the capabilities of a relational transformer by introducing HRT, which integrates hyperedge features into the transformer mechanism, promoting attention weights to focus on group-wise relations. In addition, we propose an Adaptive Group Estimator (AGE) designed to infer complex group relations in real-world environments. Extensive experiments on three real-world datasets (NBA, SDD, and ETH-UCY) demonstrate that our method achieves state-of-the-art performance, enhancing ADE/FDE by 3.9%/11.8% on the NBA dataset. Code is available at https://github.com/gist-ailab/MART .

Automatic sleep scoring is essential for the diagnosis and treatment of sleep disorders and enables longitudinal sleep tracking in home environments. Conventionally, learning-based automatic sleep scoring on single-channel electroencephalogram (EEG) is actively studied because obtaining multi-channel signals during sleep is difficult. However, learning representation from raw EEG signals is challenging owing to the following issues: (1) sleep-related EEG patterns occur on different temporal and frequency scales and 2) sleep stages share similar EEG patterns. To address these issues, we propose an automatic Sleep scoring framework that incorporates (1) a feature Pyramid and 2) supervised Contrastive learning, named SleePyCo. For the feature pyramid, we propose a backbone network named SleePyCo-backbone to consider multiple feature sequences on different temporal and frequency scales. Supervised contrastive learning allows the network to extract class discriminative features by minimizing the distance between intra-class features and simultaneously maximizing that between inter-class features. Comparative analyses on four public datasets demonstrate that SleePyCo consistently outperforms existing frameworks based on single-channel EEG. Extensive ablation experiments show that SleePyCo exhibited an enhanced overall performance, with significant improvements in discrimination between sleep stages, especially for N1 and rapid eye movement (REM). Source code is available at https://github.com/gist-ailab/SleePyCo.

Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods typically relied on the highly activated feature map outputted by the network. In this study, we revealed that the norm of the feature map obtained from a block other than the last block can serve as a better indicator for OOD detection. To leverage this insight, we propose a simple framework that comprises two metrics: FeatureNorm, which computes the norm of the feature map, and NormRatio, which calculates the ratio of FeatureNorm for ID and OOD samples to evaluate the OOD detection performance of each block. To identify the block that provides the largest difference between FeatureNorm of ID and FeatureNorm of OOD, we create jigsaw puzzles as pseudo OOD from ID training samples and compute NormRatio, selecting the block with the highest value. After identifying the suitable block, OOD detection using FeatureNorm outperforms other methods by reducing FPR95 by up to 52.77% on CIFAR10 benchmark and up to 48.53% on ImageNet benchmark. We demonstrate that our framework can generalize to various architectures and highlight the significance of block selection, which can also improve previous OOD detection methods. Our code is available at https://github.com/gistailab/block-selection-for-OOD-detection.

Previous neural architecture search (NAS) approaches for mobile platforms have achieved great success in designing a slim-but-accurate neural network that is generally well-matched to a single computing unit such as a CPU or GPU. However, as recent mobile devices consist of multiple heterogeneous computing units, the next main challenge is to maximize both accuracy and efficiency by fully utilizing multiple available resources. We propose an ensemble-like approach with intermediate feature aggregations, namely synchronizations, for active collaboration between individual models on a mobile device. A main challenge is to determine the optimal synchronization strategies for achieving both performance and efficiency. To this end, we propose SyncNAS to automate the exploration of synchronization strategies for collaborative neural architectures that maximize utilization of heterogeneous computing units on a target device. We introduce a novel search space for synchronization strategy and apply Monte Carlo tree search (MCTS) algorithm to improve the sampling efficiency and reduce the search cost. On ImageNet, our collaborative model based on MobileNetV2 achieves 2.7% top-1 accuracy improvement within the baseline latency budget. Under the reduced target latency down to half, our model maintains higher accuracy than its baseline model, owing to the enhanced utilization and collaboration. As an impact of MCTS, SyncNAS reduces its search cost by up to 21x in searching for the optimal strategy.

Distributing workloads that cannot be handled by a single edge device across multiple edge devices is a promising solution that minimizes the inference latency of deep learning applications by exploiting model parallelism. Several prior solutions have been proposed to partition target models efficiently, but most studies have focused on finding the optimal fused layer configurations, which minimize the data-transfer overhead between layers. However, as recent deep learning network models have become more complex and the ability to deploy them quickly has become a key challenge, the search for the best fused layer configurations of target models has become a major requirement. To solve this problem, we propose a lightweight model partitioning framework called Legion to find the optimal fused layer configurations with minimal profiling execution trials. By finding the optimal configurations using cost matrix construction and wild card selection, the experimental results showed that Legion achieved a similar performance to the full configuration search at a fraction of the search time. Moreover, Legion performed effectively even on a group of heterogeneous target devices by introducing a per-device cost-related matrix construction. With three popular networks, Legion shows only 3.4% performance loss as compared to a full searching scheme (FSS), on various different device configurations consisting of up to six heterogeneous devices, and minimizes the profiling overhead by 48.7× on average.