An Efficient Human Instance-Guided Framework for Video Action Recognition

Lee, Sang Hoon; Kim, Doyoung; Wee, Dongyoon; Lee, Sanghoon

doi:10.3390/s21248309

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…Therefore, ResNet-50-FPN formed our backbone network. We utilized the same ResNet-50-FPN backbone structure as described previously [43]. As opposed to Mask R-CNN, keypoint RCNN encodes a keypoint (instead of the entire mask) of the detected object.…”

Section: Modelmentioning

confidence: 99%

Automated Assessment of Radiographic Bone Loss in the Posterior Maxilla Utilizing a Multi-Object Detection Artificial Intelligence Algorithm

Vollmer

Lang

et al. 2023

Applied Sciences

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Periodontitis is one of the most prevalent diseases worldwide. The degree of radiographic bone loss can be used to assess the course of therapy or the severity of the disease. Since automated bone loss detection has many benefits, our goal was to develop a multi-object detection algorithm based on artificial intelligence that would be able to detect and quantify radiographic bone loss using standard two-dimensional radiographic images in the maxillary posterior region. This study was conducted by combining three recent online databases and validating the results using an external validation dataset from our organization. There were 1414 images for training and testing and 341 for external validation in the final dataset. We applied a Keypoint RCNN with a ResNet-50-FPN backbone network for both boundary box and keypoint detection. The intersection over union (IoU) and the object keypoint similarity (OKS) were used for model evaluation. The evaluation of the boundary box metrics showed a moderate overlapping with the ground truth, revealing an average precision of up to 0.758. The average precision and recall over all five folds were 0.694 and 0.611, respectively. Mean average precision and recall for the keypoint detection were 0.632 and 0.579, respectively. Despite only using a small and heterogeneous set of images for training, our results indicate that the algorithm is able to learn the objects of interest, although without sufficient accuracy due to the limited number of images and a large amount of information available in panoramic radiographs. Considering the widespread availability of panoramic radiographs as well as the increasing use of online databases, the presented model can be further improved in the future to facilitate its implementation in clinics.

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Section: Modelmentioning

confidence: 99%

Automated Assessment of Radiographic Bone Loss in the Posterior Maxilla Utilizing a Multi-Object Detection Artificial Intelligence Algorithm

Vollmer

Lang

et al. 2023

Applied Sciences

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“…Regarding the application of AI in guiding puncture sites during EUS-FNA/B, several limitations and shortcomings need to be considered beyond the previously discussed data standardization issue. One critical limitation is that AI is challenging for dynamic image recognition [91]. EUS images are susceptible to external elements that can cause image jitter and displacement, such as a patient's breathing and heartbeat.…”

Section: The Limitations and Shortages Of Artificial Intelligence In ...mentioning

confidence: 99%

Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges

Qin,

Ran,

Chen

et al. 2023

Diagnostics

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Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.

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“…Ref. [16] uses human boxes and key points to represent instance-level features, and the action region features of this framework are used as the input of the temporal action head network, which makes the framework more discriminative. The author of [17] proposed a multi-scale feature extraction method used to extract richer feature information.…”

Section: Action Recognitionmentioning

confidence: 99%

Temporal Context Modeling Network with Local-Global Complementary Architecture for Temporal Proposal Generation

Yuan

Yang²,

Luo³

et al. 2022

Electronics

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Temporal Action Proposal Generation (TAPG) is a promising but challenging task with a wide range of practical applications. Although state-of-the-art methods have made significant progress in TAPG, most ignore the impact of the temporal scales of action and lack the exploitation of effective boundary contexts. In this paper, we propose a simple but effective unified framework named Temporal Context Modeling Network (TCMNet) that generates temporal action proposals. TCMNet innovatively uses convolutional filters with different dilation rates to address the temporal scale issue. Specifically, TCMNet contains a BaseNet with dilated convolutions (DBNet), an Action Completeness Module (ACM), and a Temporal Boundary Generator (TBG). The DBNet aims to model temporal information. It handles input video features through different dilated convolutional layers and outputs a feature sequence as the input of ACM and TBG. The ACM aims to evaluate the confidence scores of densely distributed proposals. The TBG is designed to enrich the boundary context of an action instance. The TBG can generate action boundaries with high precision and high recall through a local–global complementary structure. We conduct comprehensive evaluations on two challenging video benchmarks: ActivityNet-1.3 and THUMOS14. Extensive experiments demonstrate the effectiveness of the proposed TCMNet on tasks of temporal action proposal generation and temporal action detection.

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An Efficient Human Instance-Guided Framework for Video Action Recognition

Cited by 7 publications

References 42 publications

Automated Assessment of Radiographic Bone Loss in the Posterior Maxilla Utilizing a Multi-Object Detection Artificial Intelligence Algorithm

Automated Assessment of Radiographic Bone Loss in the Posterior Maxilla Utilizing a Multi-Object Detection Artificial Intelligence Algorithm

Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges

Temporal Context Modeling Network with Local-Global Complementary Architecture for Temporal Proposal Generation

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