Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (\emph {eg,} social …

Abstract Referring Expression Segmentation (RES) aims to generate a segmentation mask
for the object described by a given language expression. Existing classic RES datasets and …

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 …

In this paper, we present a neat yet effective transformer-based framework for visual
grounding, namely TransVG, to address the task of grounding a language query to the …

Existing referring understanding tasks tend to involve the detection of a single text-referred
object. In this paper, we propose a new and general referring understanding task, termed …

We propose a Vision-Language Transformer (VLT) framework for referring segmentation to
facilitate deep interactions among multi-modal information and enhance the holistic …

Image-text matching plays a critical role in bridging the vision and language, and great
progress has been made by exploiting the global alignment between image and sentence …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Visual grounding is a task to locate the target indicated by a natural language expression.
Existing methods extend the generic object detection framework to this problem. They base …

We consider the problem of localizing a spatio-temporal tube in a video corresponding to a
given text query. This is a challenging task that requires the joint and efficient modeling of …