With the improvement of the intelligence degree of textile and garment industry, with the help of advanced information technology, using computerimagerecognition method to realize automatic fabric recognition, can i...
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Multi-label recognition is a critical task in artificial intelligence, aiming to identify every object present in an image. Designing a multi-label classifier is a nontrivial task both from data collection and modelin...
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In the advancing fields of computer vision and deep learning, video dynamic object removal technology is an important research area. The goal of this technology is to remove specific dynamic objects from a continuous ...
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It’s challenging to optimize offline handwritten Chinese recognition as a topic of patternrecognition as the consequence of the complex structures of Chinese characters. To solve this problem, many data pre-processi...
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The substantial scale variation and intra-class diversity within remote sensing imagery pose significant challenges for semantic segmentation, rendering methods developed for natural images inapplicable. These challen...
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Sign language has long been a fundamental mode of communication for deaf and mute individuals, serving as a crucial tool for inclusivity and interaction. Nonetheless, communication barriers persist as many individuals...
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Semantic segmentation is a challenging task since it requires excessively more low-level spatial information of the image compared to other computer vision problems. The accuracy of pixel-level classification can be a...
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ISBN:
(纸本)9781665487399
Semantic segmentation is a challenging task since it requires excessively more low-level spatial information of the image compared to other computer vision problems. The accuracy of pixel-level classification can be affected by many factors, such as imaging limitations and the ambiguity of object boundaries in an image. Conventional methods exploit three-channel RGB images captured in the visible spectrum with deep neural networks (DNN). Thermal images can significantly contribute during the segmentation since thermal imaging cameras are capable of capturing details despite the weather and illumination conditions. Using infrared spectrum in semantic segmentation has many real-world use cases, such as autonomous driving, medical imaging, agriculture, defense industry, etc. Due to this wide range of use cases, designing accurate semantic segmentation algorithms with the help of infrared spectrum is an important challenge. One approach is to use both visible and infrared spectrum images as inputs. These methods can accomplish higher accuracy due to enriched input information, with the cost of extra effort for the alignment and processing.of multiple inputs. Another approach is to use only thermal images, enabling less hardware cost for smaller use cases. Even though there are multiple surveys on semantic segmentation methods, the literature lacks a comprehensive survey centered explicitly around semantic segmentation using infrared spectrum. This work aims to fill this gap by presenting algorithms in the literature and categorizing them by their input images.
The efficacy of facial recognition systems that utilize deep learning techniques has led to significant concerns over privacy, since they possess the capability to facilitate unauthorized monitoring of individuals in ...
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Abundant redundancies exist in video streams, thereby pointing to opportunities to save computations. Towards this end, we propose the Adaptive Network across Time (ANT) framework to harness these redundancies for red...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
Abundant redundancies exist in video streams, thereby pointing to opportunities to save computations. Towards this end, we propose the Adaptive Network across Time (ANT) framework to harness these redundancies for reducing the computational cost of video processing. Unlike most dynamic networks that adapt their structures to different static inputs, our method adapts networks along the temporal dimension. By inspecting the semantic differences between frames, the proposed ANT chooses a purpose-fit network at test time to reduce overall computation, i.e., switching to a smaller network when observing mild differences. The proposed ANT adapts the structured networks within a supernet, making it hardware-friendly and therefore achieves actual acceleration in real-world scenarios. The proposed ANT is powered by (1) a fusion module that utilizes the past features and (2) a dynamic gate to adjust the network in a predictive fashion with negligible extra cost. To ensure the generality of each subnet and the gate's fairness, we propose a two-stage training scheme. We first train a weight-sharing supernet and then jointly train fusion modules and gates. Evaluation of the video detection task with the modern EfficientDet reveals the effectiveness of our approach.
Weed is one of the major causes for wheat yields reduction. Accurate and efficient classification and identification of wheat and weed could assist farmers to control weed effectively, which is of great significance f...
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