Cross-modal noise-robust learning is a challenging task since noisy correspondence is hard
to recognize and rectify. Due to the cumulative and unavoidable negative impact of …

Reliable uncertainty estimation has become a crucial requirement for the industrial
deployment of deep learning algorithms, particularly in high-risk applications such as …

Fully supervised change detection (CD) methods have achieved significant advancements
in performance, yet they depend severely on acquiring costly pixel-level labels. Considering …

Cross-lingual cross-modal retrieval (CCR) aims to retrieve visually relevant content based
on non-English queries, without relying on human-labeled cross-modal data pairs during …

Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation that
provides reliable predictive uncertainty in a single forward pass, attracting significant …

This work tackles the persistent challenge of image-text retrieval, a key problem at the
intersection of computer vision and natural language processing. Despite significant …

Mitigating the detrimental effects of noisy labels on the training process has become
increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale …

The image-recipe cross-modal retrieval task, which retrieves the relevant recipes according
to food images and vice versa, is now attracting widespread attention. There are two main …

Cross-modal image-text retrieval is a challenging task due to the inherent ambiguity
between modalities. However, most existing methods formulate this problem either with the …