Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

With the increasing demand for real-world scenarios such as robot navigation and
autonomous driving, how to achieve a good trade-off between segmentation accuracy …

Prevalent semantic segmentation solutions, despite their different network designs (FCN
based or attention based) and mask decoding strategies (parametric softmax based or pixel …

We present SegFormer, a simple, efficient yet powerful semantic segmentation framework
which unifies Transformers with lightweight multilayer perceptron (MLP) decoders …

Image segmentation is often ambiguous at the level of individual image patches and
requires contextual information to reach label consensus. In this paper we introduce …

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 …

Current semantic segmentation methods focus only on mining" local" context, ie,
dependencies between pixels within individual images, by context-aggregation modules …

We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We
start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution …

Low-light image enhancement plays very important roles in low-level vision areas. Recent
works have built a great deal of deep learning models to address this task. However, these …

MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense
geometry and semantics of a scene are inferred from a single monocular RGB image …