# HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation

arXiv: https://arxiv.org/abs/2303.15994 · HTML: https://mlsanity.com/paper/2303.15994/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 77 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 30 of 58)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [Neural Motifs: Scene Graph Parsing with Global Context](https://mlsanity.com/paper/1711.06640/) (2017)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [Visual Relationship Detection with Language Priors](https://mlsanity.com/paper/1608.00187/) (2016)
- [Learning to Compose Dynamic Tree Structures for Visual Contexts](https://mlsanity.com/paper/1812.01880/) (2018)
- [Disentangling Label Distribution for Long-tailed Visual Recognition](https://mlsanity.com/paper/2012.00321/) (2020)
- [GPS-Net: Graph Property Sensing Network for Scene Graph Generation](https://mlsanity.com/paper/2003.12962/) (2020)
- [Knowledge-Embedded Routing Network for Scene Graph Generation](https://mlsanity.com/paper/1903.03326/) (2019)
- [Scene Graph Generation from Objects, Phrases and Region Captions](https://mlsanity.com/paper/1707.09700/) (2017)
- [Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2104.00308/) (2021)
- [Panoptic Scene Graph Generation](https://mlsanity.com/paper/2207.11247/) (2022)
- [Visual Translation Embedding Network for Visual Relation Detection](https://mlsanity.com/paper/1702.08319/) (2017)
- [Explainable and Explicit Visual Reasoning over Scene Graphs](https://mlsanity.com/paper/1812.01855/) (2018)
- [SGTR: End-to-end Scene Graph Generation with Transformer](https://mlsanity.com/paper/2112.12970/) (2021)

## Built on by (most cited first, 19 of 19)

- [LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation](https://mlsanity.com/paper/2306.08736/) (2023)
- [4D Panoptic Scene Graph Generation](https://mlsanity.com/paper/2405.10305/) (2024)
- [Pair then Relation: Pair-Net for Panoptic Scene Graph Generation](https://mlsanity.com/paper/2307.08699/) (2023)
- [OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models](https://mlsanity.com/paper/2407.11213/) (2024)
- [VLPrompt: Vision-Language Prompting for Panoptic Scene Graph Generation](https://mlsanity.com/paper/2311.16492/) (2023)
- [DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation](https://mlsanity.com/paper/2403.14886/) (2024)
- [Universal Scene Graph Generation](https://mlsanity.com/paper/2503.15005/) (2025)
- [Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes](https://mlsanity.com/paper/2309.02286/) (2023)
- [AIMS: All-Inclusive Multi-Level Segmentation](https://mlsanity.com/paper/2305.17768/) (2023)
- [Synthetic Visual Genome](https://mlsanity.com/paper/2506.07643/) (2025)
- [A Fair Ranking and New Model for Panoptic Scene Graph Generation](https://mlsanity.com/paper/2407.09216/) (2024)
- [CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery](https://mlsanity.com/paper/2506.21813/) (2025)
- [From Easy to Hard: Learning Curricular Shape-aware Features for Robust Panoptic Scene Graph Generation](https://mlsanity.com/paper/2407.09191/) (2024)
- [A Review and Efficient Implementation of Scene Graph Generation Metrics](https://mlsanity.com/paper/2404.09616/) (2024)
- [Towards Flexible Visual Relationship Segmentation](https://mlsanity.com/paper/2408.08305/) (2024)
- [A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation](https://mlsanity.com/paper/2505.23451/) (2025)
- [CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations](https://mlsanity.com/paper/2506.21357/) (2025)
- [DSFlash: Comprehensive Panoptic Scene Graph Generation in Realtime](https://mlsanity.com/paper/2603.10538/) (2026)
- [Frequency-guided Multi-level Reasoning for Scene Graph Generation in Video](https://mlsanity.com/paper/2604.17298/) (2026)

## Abstract (arXiv)

Panoptic Scene Graph generation (PSG) is a recently proposed task in image scene understanding that aims to segment the image and extract triplets of subjects, objects and their relations to build a scene graph. This task is particularly challenging for two reasons. First, it suffers from a long-tail problem in its relation categories, making naive biased methods more inclined to high-frequency relations. Existing unbiased methods tackle the long-tail problem by data/loss rebalancing to favor low-frequency relations. Second, a subject-object pair can have two or more semantically overlapping relations. While existing methods favor one over the other, our proposed HiLo framework lets different network branches specialize on low and high frequency relations, enforce their consistency and fuse the results. To the best of our knowledge we are the first to propose an explicitly unbiased PSG method. In extensive experiments we show that our HiLo framework achieves state-of-the-art results on the PSG task. We also apply our method to the Scene Graph Generation task that predicts boxes instead of masks and see improvements over all baseline methods. Code is available at https://github.com/franciszzj/HiLo.