Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Transformer and its variants have become the preferred option for multimodal vision-
language paradigms. However, they struggle with tasks that demand high-dependency …

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

As an important step towards visual reasoning, visual grounding (eg, phrase localization,
referring expression comprehension/segmentation) has been widely explored. Previous …

We explore the task of language-guided video segmentation (LVS). Previous algorithms
mostly adopt 3D CNNs to learn video representation, struggling to capture long-term context …

In this work, we explore neat yet effective Transformer-based frameworks for visual
grounding. The previous methods generally address the core problem of visual grounding …

Visual grounding (VG) aims to locate a specific target in an image based on a given
language query. The discriminative information from context is important for distinguishing …

This article tackles the challenging yet important task of Visual Grounding (VG), which aims
to localize a visual region in the given image referred by a natural language query. Existing …