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Found 5 papers, 2 papers with code
Instructing Prompt-to-Prompt Generation for Zero-Shot Learning
Zero-shot learning (ZSL) aims to explore the semantic-visual interactions to discover comprehensive knowledge transferred from seen categories to classify unseen categories.
Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance
In this work, we explore the potential of resolution fields in scalable image compression and propose the reciprocal pyramid network (RPN) that fulfills the need for more adaptable and versatile compression.
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information.
EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets
This paper describes our system submitted to IEST 2018, a shared task (Klinger et al., 2018) to predict the emotion types.
EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM
This paper describes our system used in the English Emoji Prediction Task 2 at the SemEval-2018.
