2 citations · 6 across the 5 of their papers we have counts for
5 papers
Personalised Outfit Recommendation via History-aware Transformers
Myong Chol Jung, Julien Monteil, Philip Schulz +1
We present the history-aware transformer (HAT), a transformer-based model that uses shoppers' purchase history to personalise outfit predictions. The aim of this work is to recomme…
Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score
Myong Chol Jung, Joanna Dipnall, Belinda Gabbe +1
Prompt learning has emerged as an efficient and effective method for fine-tuning vision-language models such as CLIP. While many studies have explored generalisation abilities of t…
Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
Tianyu Zhu, Myong Chol Jung, Jesse Clark
Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations. However, popular training frameworks typically learn…
Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
Myong Chol Jung, He Zhao, Joanna Dipnall +1
Uncertainty estimation is an important research area to make deep neural networks (DNNs) more trustworthy. While extensive research on uncertainty estimation has been conducted wit…
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Myong Chol Jung, He Zhao, Joanna Dipnall +2
Uncertainty estimation is essential to make neural networks trustworthy in real-world applications. Extensive research efforts have been made to quantify and reduce predictive unce…