“…This particular scenario addresses the adaptability of the AI-AI collaborative ecosystem and demonstrates its potential to address real-world challenges by utilizing sensor data, exchanging meaningful dialogues, and finally translating the conclusion into effective actions through actuators. The self-contained decision-making capabilities of the proposed architecture present a futuristic vision for advancing AI applications in the real world, which can address problems in wide range of domains such as indoor positioning [ 30 ], robotics [ 31 ], synthetic aperture radar [ 32 ], scheduling [ 33 ], etc. Fig.…”
Section: Discussion Conclusion and Future Workmentioning
“…This particular scenario addresses the adaptability of the AI-AI collaborative ecosystem and demonstrates its potential to address real-world challenges by utilizing sensor data, exchanging meaningful dialogues, and finally translating the conclusion into effective actions through actuators. The self-contained decision-making capabilities of the proposed architecture present a futuristic vision for advancing AI applications in the real world, which can address problems in wide range of domains such as indoor positioning [ 30 ], robotics [ 31 ], synthetic aperture radar [ 32 ], scheduling [ 33 ], etc. Fig.…”
Section: Discussion Conclusion and Future Workmentioning
“…Inspired by related works [34][35][36][37][38], we expect our framework to be applied in spatiotemporal action detection and security surveillance.…”
Spatio-temporal action detection (STAD) is a task receiving widespread attention and has numerous application scenarios, such as video surveillance and smart education. Current studies follow a localization-based two-stage detection paradigm, which exploits a person detector for action localization and a feature processing model with a classifier for action classification. However, many issues occur due to the imbalance between task settings and model complexity in STAD. Firstly, the model complexity of heavy offline person detectors adds to the inference overhead. Secondly, the frame-level actor proposals are incompatible with the video-level feature aggregation and Region-of-Interest feature pooling in action classification, which limits the detection performance under diverse action motions and results in low detection accuracy. In this paper, we propose a tracking-based two-stage spatio-temporal action detection framework called TrAD. The key idea of TrAD is to build video-level consistency and reduce model complexity in our STAD framework by generating action track proposals among multiple video frames instead of actor proposals in a single frame. In particular, we utilize tailored tracking to simulate the behavior of human cognitive actions and used the captured motion trajectories as video-level proposals. We then integrate a proposal scaling method and a feature aggregation module into action classification to enhance feature pooling for detected tracks. Evaluations in the AVA dataset demonstrate that TrAD achieves SOTA performance with 29.7 mAP, while also facilitating a 58% reduction in overall computation compared to SlowFast.
“…Zhou et al [28] introduced an improved lightweight RetinaNet for ship detection in SAR images. Wang et al [29] proposed a novel SAR ship detection method, NAS-YOLOX, which leverages the efficient feature fusion of the neural architecture search feature pyramid network (NAS-FPN) and the effective feature extraction of the multi-scale attention mechanism. Tang et al [30] proposed a Pyramid Pooling Attention Network (PPA-Net) for SAR multi-scale ship detection.…”
In recent years, deep learning has found widespread application in SAR image object detection. However, when detecting multi-scale targets against complex backgrounds, these models often struggle to strike a balance between accuracy and speed. Furthermore, there is a continuous need to enhance the performance of current models. Hence, this paper proposes LRMSNet, a new multi-scale target detection model designed specifically for SAR images in complex backgrounds. Firstly, the paper introduces an attention module designed to enhance contextual information aggregation and capture global features, which is integrated into a backbone network with an expanded receptive field for improving SAR image feature extraction. Secondly, this paper develops an information aggregation module to effectively fuse different feature layers of the backbone network. Lastly, to better integrate feature information at various levels, this paper designs a multi-scale aggregation network. We validate the effectiveness of our method on three different SAR object detection datasets (MSAR-1.0, SSDD, and HRSID). Experimental results demonstrate that LRMSNet achieves outstanding performance with a mean average accuracy (mAP) of 95.2%, 98.9%, and 93.3% on the MSAR-1.0, SSDD, and HRSID datasets, respectively, with only 3.46 M parameters and 12.6 G floating-point operation cost (FLOPs). When compared with existing SAR object detection models on the MSAR-1.0 dataset, LRMSNet achieves state-of-the-art (SOTA) performance, showcasing its superiority in addressing SAR detection challenges in large-scale complex environments and across various object scales.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
