# Unified Perceptual Parsing for Scene Understanding

arXiv: https://arxiv.org/abs/1807.10221 · HTML: https://mlsanity.com/paper/1807.10221/

## Key facts

- Cited by 1,271 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

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## Abstract (arXiv)

Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes. Models are available at urlhttps://github.com/CSAILVision/unifiedparsing.