Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

敲竹杠

ATOPY

Aluminium, Aluminiumguß, Duralumin, Elektronfficiently remains a challenging task due to the reduced size of the objects, low contrast to their surroundings, and potential occlusions. To tackle this issue, we proposed a method for detecting small objects in object detection tasks, including a new strategy for balancing positive and negative s

phlegm

Sonstige Metalle und Legierungenowever, domain adaptation presents a significant challenge due to the impact of weather, lighting, and scene context on object detection models. To address this issue, we propose a new method that utilizes pseudo-labels. Our approach involves two modules: the Category-Adversarial-Adaptive (CAA) and

output

https://doi.org/10.1007/978-3-662-25789-0source images. When it comes to visible-infrared image pairs, however, the visible images are prone to illumination and visibility and there may be a lot of interference information and little useful information. We suggest performing common feature enhancement and spatial cross attention sequential

慷慨援助

https://doi.org/10.1007/978-3-662-25789-0n federated learning. Previous studies have explored the possibility of inference attacks on split learning. However, existing methods suffer from unrealistic threat models and poor robustness against defensive techniques. Remarkably, we propose a novel and general framework to perform inference att

委屈

https://doi.org/10.1007/978-3-7091-3761-1ted examples. Transfer learning has been identified as an effective method for solving this task, as it allows the model to learn better feature embedding. In this paper, we utilize the Faster R-CNN framework and its potential to address FSOD tasks. We optimize the fine-tuning process of the detecto

Mercurial

saturated-fat

重画只能放弃

Herbert A. Mang,Günter Hofstetterre hard to be detected due to few points, hindering overall detection accuracy. To address this issue, we propose a novel two-stage 3D object detection method, which introduces an attention strategy to enhance key structure information of objects, so as to promote overall detection accuracy, especia

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