6 accepted papers
Current text-only image captioning methods leverage the shared feature space of CLIP to train zero-shot image captioning using text data only, leaving feature associations and contextual understanding not fully explored. Neurological studies have revealed that the anterior temporal lobes of the brai…
The integration of Large Language Models (LLMs) with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query. Existing LLM-based retrieval methods generate numeric or content-based DocIDs to retrieve docs/ads. However, the one-to-few m
Few-shot learning has been extensively applied in current remote sensing image classification, enabling rapid identification of new classes by leveraging prior knowledge effectively. However, current methods mainly rely on image modality to address the issue of low intra-class similarity and high in…
Learning by exploration makes reinforcement learning (RL) potentially attractive for many real-world applications. However, this learning process makes RL inherently too vulnerable to be used in real-world applications where safety is of utmost importance. Most prior studies consider exploration at…
Interactive argument pair identification is an emerging research task for argument mining, aiming to identify whether two arguments are interactively related. It is pointed out that the context of the argument is essential to improve identification performance. However, current context-based methods…
Multimodal pre-training models, such as LXMERT, have achieved excellent results in downstream tasks. However, current pre-trained models require large amounts of training data and have huge model sizes, which make them impossible to apply in low-resource situations. How to obtain similar or even bet…