The success of contrastive learning is well known to be dependent on data augmentation.
Although the degree of data augmentations has been well controlled by utilizing pre-defined …

Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

With the rise of short videos, the demand for selecting appropriate background music (BGM)
for a video has increased significantly, video-music retrieval (VMR) task gradually draws …

A well-calibrated neural model produces confidence (probability outputs) closely
approximated by the expected accuracy. While prior studies have shown that mixup training …

Recent advances in NLP are brought by a range of large-scale pretrained language models
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …

Data augmentation is an effective approach to tackle over-fitting. Many previous works have
proposed different data augmentations strategies for NLP, such as noise injection, word …

Prompt tuning, or the conditioning of a frozen pretrained language model (PLM) with soft
prompts learned from data, has demonstrated impressive performance on a wide range of …

This paper proposes a simple yet effective interpolation-based data augmentation approach
termed DoubleMix, to improve the robustness of models in text classification. DoubleMix first …

Pre-trained multi-modal models, such as CLIP, provide transferable embeddings and show
promising results in diverse applications. However, the analysis of learned multi-modal …

Advancements in conversational systems have revolutionized information access,
surpassing the limitations of single queries. However, developing dialogue systems requires …