A multitask joint framework for real-time person search

Li, Ye; Yin, Kangning; Liang, Jie; Wang, Qianqian; Guo, Yin

doi:10.1007/s00530-022-00982-y

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…To begin with, for feature representation of both 2D images and 3D models, a better backbone is always encouraged, which draws our attention to the trendy vision transformers (ViT) recently. It has proved to be a success in many relative computer vision and natural language processing (NLP) such as video event detection [16], pedestrian detection [17], person search [18,19], and text classification [20]. ViT takes the image patch or word embedding as a sequence of tokens, and applies the self-attention mechanism to capture the internal relationships thus obtaining strong feature representation connected with downstream tasks.…”

Section: Introductionmentioning

confidence: 99%

View-target relation-guided unsupervised 2D image-based 3D model retrieval via transformer

Chang,

Zhang,

Shao

2023

Multimedia Systems

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Unsupervised 2D image-based 3D model retrieval aims at retrieving images from the gallery of 3D models by the given 2D images. Despite the encouraging progress made in this task, there are still two significant limitations: (1) feature alignment of 2D images and 3D model gallery is still difficult due to the huge gap between the two modalities. (2) The important view information in the 3D model gallery was ignored by the prior arts, which led to inaccurate results. To alleviate these limitations, inspired by the success of vision transformers (ViT) in a great variety of vision tasks, in this paper, we propose an end-to-end 3D model retrieval architecture on top of ViT, termly transformer-based 3D model retrieval network (T3DRN). In addition, to take advantage of the valuable view information of 3D models, we present an attentive module in T3DRN named shared view-guided attentive module (SVAM) to guide the learning of the alignment features. The proposed method is tested on the challenging dataset, MI3DOR-1. The extensive experimental results have proved the superiority of our proposed method to state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

View-target relation-guided unsupervised 2D image-based 3D model retrieval via transformer

Chang,

Zhang,

Shao

2023

Multimedia Systems

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“…There are numerous novel neural network architectures for lightweight YOLO algorithms, such as MobileNetV3 with the SE (squeeze excitation) attention mechanism and hand switch function, PP-LCNet with a similar structure, Ghost-Net, etc. [12][13][14][15][16][17]. B Ma et al proposed a Gaussian distance intersection over union (GDIoU) loss function and applied it to the YOLOV4 network [18], which increased the average precision by 7.37%.…”

Section: Introductionmentioning

confidence: 99%

YOLO-SS-Large: A Lightweight and High-Performance Model for Defect Detection in Substations

Wang,

Yang,

Zhou

et al. 2023

Sensors

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With the development of deep fusion intelligent control technology and the application of low-carbon energy, the number of renewable energy sources connected to the distribution grid has been increasing year by year, gradually replacing traditional distribution grids with active distribution grids. In addition, as an important component of the distribution grid, substations have a complex internal environment and numerous devices. The problems of untimely defect detection and slow response during intelligent inspections are particularly prominent, posing risks and challenges to the safe and stable operation of active distribution grids. To address these issues, this paper proposes a high-performance and lightweight substation defect detection model called YOLO-Substation-large (YOLO-SS-large) based on YOLOv5m. The model improves lightweight performance based upon the FasterNet network structure and obtains the F-YOLOv5m model. Furthermore, in order to enhance the detection performance of the model for small object defects in substations, the normalized Wasserstein distance (NWD) and complete intersection over union (CIoU) loss functions are weighted and fused to design a novel loss function called NWD-CIoU. Lastly, based on the improved model mentioned above, the dynamic head module is introduced to unify the scale-aware, spatial-aware, and task-aware attention of the object detection heads of the model. Compared to the YOLOv5m model, the YOLO-SS-Large model achieves an average precision improvement of 0.3%, FPS enhancement of 43.5%, and parameter reduction of 41.0%. This improved model demonstrates significantly enhanced comprehensive performance, better meeting the requirements of the speed and precision for substation defect detection, and plays an important role in promoting the informatization and intelligent construction of active distribution grids.

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Temporal Attention Framework Based on Occlusion Localization for Video Person Re-ID

Li,

Shuai,

Deng

et al. 2024

Lecture Notes in Electrical Engineering

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A multitask joint framework for real-time person search

Cited by 4 publications

References 42 publications

View-target relation-guided unsupervised 2D image-based 3D model retrieval via transformer

View-target relation-guided unsupervised 2D image-based 3D model retrieval via transformer

YOLO-SS-Large: A Lightweight and High-Performance Model for Defect Detection in Substations

Temporal Attention Framework Based on Occlusion Localization for Video Person Re-ID

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