This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

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 …

In this paper, we present a new sequence-to-sequence learning framework for visual
tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem …

All instance perception tasks aim at finding certain objects specified by some queries such
as category names, language expressions, and target annotations, but this complete field …

Abstract Most Large Vision-Language Models (LVLMs) enjoy the same vision vocabulary, ie,
CLIP, for common vision tasks. However, for some special task that needs dense and fine …

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

We introduce Ferret, a new Multimodal Large Language Model (MLLM) capable of
understanding spatial referring of any shape or granularity within an image and accurately …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

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

In this paper, we propose a simple yet universal network termed SeqTR for visual grounding
tasks, eg, phrase localization, referring expression comprehension (REC) and segmentation …