# GINet: Graph Interaction Network for Scene Parsing

arXiv: https://arxiv.org/abs/2009.06160 · HTML: https://mlsanity.com/paper/2009.06160/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 13 of 13)

- [CRIS: CLIP-Driven Referring Image Segmentation](https://mlsanity.com/paper/2111.15174/) (2021)
- [SegViT: Semantic Segmentation with Plain Vision Transformers](https://mlsanity.com/paper/2210.05844/) (2022)
- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis](https://mlsanity.com/paper/2103.05558/) (2021)
- [StructToken : Rethinking Semantic Segmentation with Structural Prior](https://mlsanity.com/paper/2203.12612/) (2022)
- [SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers](https://mlsanity.com/paper/2306.06289/) (2023)
- [Fully Transformer Networks for Semantic Image Segmentation](https://mlsanity.com/paper/2106.04108/) (2021)
- [Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2204.13314/) (2022)
- [Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images](https://mlsanity.com/paper/2101.08910/) (2021)
- [Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation](https://mlsanity.com/paper/2103.04235/) (2021)
- [PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation](https://mlsanity.com/paper/2305.00671/) (2023)
- [Feature Selective Transformer for Semantic Image Segmentation](https://mlsanity.com/paper/2203.14124/) (2022)
- [Neovascularization Segmentation via a Multilateral Interaction-Enhanced Graph Convolutional Network](https://mlsanity.com/paper/2508.03197/) (2025)

## Abstract (arXiv)

Recently, context reasoning using image regions beyond local convolution has shown great potential for scene parsing. In this work, we explore how to incorporate the linguistic knowledge to promote context reasoning over image regions by proposing a Graph Interaction unit (GI unit) and a Semantic Context Loss (SC-loss). The GI unit is capable of enhancing feature representations of convolution networks over high-level semantics and learning the semantic coherency adaptively to each sample. Specifically, the dataset-based linguistic knowledge is first incorporated in the GI unit to promote context reasoning over the visual graph, then the evolved representations of the visual graph are mapped to each local representation to enhance the discriminated capability for scene parsing. GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph. We perform full ablation studies to demonstrate the effectiveness of each component in our approach. Particularly, the proposed GINet outperforms the state-of-the-art approaches on the popular benchmarks, including Pascal-Context and COCO Stuff.