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Titlebook: Recent Trends in Analysis of Images, Social Networks and Texts; 11th International C Dmitry I. Ignatov,Michael Khachay,Sergey Zagoruyko Con

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發(fā)表于 2025-3-25 04:54:23 | 只看該作者
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發(fā)表于 2025-3-25 09:24:47 | 只看該作者
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發(fā)表于 2025-3-25 11:41:10 | 只看該作者
Gesture Recognition on?Video Dataacross a sequence of frames..Current approaches to hand gesture recognition face limitations. Some methods rely solely on visual inputs, processing them without considering occlusion and illumination issues, leading to performance degradation. Others employ wearable sensors, which can be bulky and p
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發(fā)表于 2025-3-25 17:44:38 | 只看該作者
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發(fā)表于 2025-3-25 20:05:09 | 只看該作者
Learning Facial Expression Recognition In-the-Wild from?Synthetic Data Based on?an?Ensemble of?Lightition. We study various ensemble approaches that combine the lightweight MT-EmotiEffNet, attention-based transformer, and the graph-based model. The models have been implemented on an edge device (Jetson Nano) to be integrated into a demo application for video analytics to evaluate their performance
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發(fā)表于 2025-3-26 01:36:29 | 只看該作者
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發(fā)表于 2025-3-26 11:10:47 | 只看該作者
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發(fā)表于 2025-3-26 16:00:41 | 只看該作者
30#
發(fā)表于 2025-3-26 18:36:23 | 只看該作者
Prompt-Tuning for?Targeted Sentiment Analysis in?Russian-tuning BERT-like models were explored. The best result was achieved when incorporating external knowledge into the prompt verbalizer. We demonstrate that prompt-tuning methods help achieve high results while being less computationally intensive than other fine-tuning and ensembling strategies.
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