Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

Referring image segmentation aims to segment a referent via a natural linguistic expression.
Due to the distinct data properties between text and image, it is challenging for a network to …

We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and
propose the SegViT. Previous ViT-based segmentation networks usually learn a pixel-level …

The demand of applying semantic segmentation model on mobile devices has been
increasing rapidly. Current state-of-the-art networks have enormous amount of parameters …

Unsupervised domain adaptive object detection (UDA-OD) aims to learn a detector by
generalizing knowledge from a labeled source domain to an unlabeled target domain …

This paper investigates the capability of plain Vision Transformers (ViTs) for semantic
segmentation using the encoder–decoder framework and introduce SegViTv2. In this study …

Scene understanding is a critical problem in computer vision. In this paper, we propose a 3D
point-based scene graph generation (SGGpoint) framework to effectively bridge perception …

In previous deep-learning-based methods, semantic segmentation has been regarded as a
static or dynamic per-pixel classification task, ie, classify each pixel representation to a …

Due to the practical significance in smart video surveillance systems, Text-Based Person
Search (TBPS) has been one of the research hotspots recently, which refers to searching for …

Transformers have shown impressive performance in various natural language processing
and computer vision tasks, due to the capability of modeling long-range dependencies …