Transformer, an attention-based encoder–decoder model, has already revolutionized the
field of natural language processing (NLP). Inspired by such significant achievements, some …

Abstract Large-scale Vision-Language Models, such as CLIP, learn powerful image-text
representations that have found numerous applications, from zero-shot classification to text …

Abstract Vision-Language Pre-training (VLP) models have shown promising capabilities in
grounding natural language in image data, facilitating a broad range of cross-modal tasks …

Large language models (LLMs) have demonstrated excellent zero-shot generalization to
new language tasks. However, effective utilization of LLMs for zero-shot visual question …

Abstract The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its
application to diverse downstream vision tasks. To improve its capacity on downstream …

This paper revisits visual representation in knowledge-based visual question answering
(VQA) and demonstrates that using regional information in a better way can significantly …

In this article, we introduce the task of visual grounding for remote sensing data (RSVG).
RSVG aims to localize the referred objects in remote sensing (RS) images with the guidance …

Abstract Existing Referring Image Segmentation (RIS) methods typically require expensive
pixel-level or box-level annotations for supervision. In this paper, we observe that the …

Large language models (LLMs) have demonstrated excellent zero-shot generalization to
new language tasks. However, effective utilization of LLMs for zero-shot visual question …

Text-video retrieval is an important multi-modal learning task, where the goal is to retrieve
the most relevant video for a given text query. Recently, pre-trained models, eg, CLIP, show …