Large-scale vision-language models (VLMs) like CLIP successfully find correspondences
between images and text. Through the standard deterministic mapping process, an image or …

Image-Text Matching (ITM) task, a fundamental vision-language (VL) task, suffers from the
inherent ambiguity arising from multiplicity and imperfect annotations. Deterministic …

Cross-modal Retrieval methods build similarity relations between vision and language
modalities by jointly learning a common representation space. However, the predictions are …

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 …

Due to high labeling cost, it is inevitable to introduce a certain proportion of noisy
correspondence into visual-text datasets, resulting in poor model robustness for cross-modal …

It is essential to delve into the strategy of multimodal model pre-training, which is an obvious
impact on downstream tasks. Currently, clustering learning has achieved noteworthy …

Image-text retrieval (ITR) has been one of the primary tasks in cross-modal retrieval, serving
as a crucial bridge between computer vision and natural language processing. Significant …

Image-text retrieval is one of the major tasks of cross-modal retrieval. Several approaches
for this task map images and texts into a common space to create correspondences between …

Compared with RGB images, hyperspectral images (HSIs) offer a distinct advantage in that
they can record continuous spectral bands of light reflectance in each pixel, reflecting the …

With the advancement of deep learning, the task of image-text retrieval has received
widespread attention for addressing the semantic heterogeneity in multimodal data …