4 accepted papers
Nighttime semantic segmentation is an indispensable component in practical applications, such as automated vehicles. However, it is often hindered by the lack of annotations due to interference caused by inadequate lighting or exposure. To overcome these difficulties, we propose a Dual-Path Consiste…
Multimodal learning presents significant challenges arising from the unpredictable absence of modalities during both training and testing phases. Existing recovery methods struggle to leverage the available data, which can introduce additional noise during the recovery process and degrade performanc…
Multi-modal aspect-oriented sentiment classification (MASC) is a fine-grain task, which aims to detect the sentiment polarity of specific aspect. However, conventional studies suffer from two issues. It is difficult to collect the annotated multi-modal data in fine-grained domains. Meanwhile, the lo…
Medical vision-language representation learning has garnered considerable attention owing to its applicability to extracting generic representations from the image and text modality. However, it still remains challenging to acquire a more comprehensive understanding of intra- and inter-modal semanti…