A separable prompt learning strategy on CLIP's text encoder enables competitive or superior generalizable performance in cross-dataset and cross-method face forgery detection.
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3 Pith papers cite this work. Polarity classification is still indexing.
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An integrated node transformer and BERT sentiment model reports 0.80% MAPE for one-day stock predictions on 20 S&P 500 stocks, beating ARIMA and LSTM baselines.
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Generalizable Face Forgery Detection via Separable Prompt Learning
A separable prompt learning strategy on CLIP's text encoder enables competitive or superior generalizable performance in cross-dataset and cross-method face forgery detection.
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Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis
An integrated node transformer and BERT sentiment model reports 0.80% MAPE for one-day stock predictions on 20 S&P 500 stocks, beating ARIMA and LSTM baselines.
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