# CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation

arXiv: https://arxiv.org/abs/2009.07526 · HTML: https://mlsanity.com/paper/2009.07526/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 30 of 62)

- [RelTR: Relation Transformer for Scene Graph Generation](https://mlsanity.com/paper/2201.11460/) (2022)
- [Scene Graph Generation: A Comprehensive Survey](https://mlsanity.com/paper/2201.00443/) (2022)
- [Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2203.09811/) (2022)
- [The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation](https://mlsanity.com/paper/2206.03014/) (2022)
- [Fine-Grained Scene Graph Generation with Data Transfer](https://mlsanity.com/paper/2203.11654/) (2022)
- [Panoptic Scene Graph Generation with Semantics-Prototype Learning](https://mlsanity.com/paper/2307.15567/) (2023)
- [Prototype-based Embedding Network for Scene Graph Generation](https://mlsanity.com/paper/2303.07096/) (2023)
- [Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World](https://mlsanity.com/paper/2303.13233/) (2023)
- [Fine-Grained Predicates Learning for Scene Graph Generation](https://mlsanity.com/paper/2204.02597/) (2022)
- [Iterative Scene Graph Generation with Generative Transformers](https://mlsanity.com/paper/2211.16636/) (2022)
- [Unbiased Scene Graph Generation via Two-stage Causal Modeling](https://mlsanity.com/paper/2307.05276/) (2023)
- [Meta Spatio-Temporal Debiasing for Video Scene Graph Generation](https://mlsanity.com/paper/2207.11441/) (2022)
- [NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation](https://mlsanity.com/paper/2207.13316/) (2022)
- [HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation](https://mlsanity.com/paper/2303.15994/) (2023)
- [Vision Relation Transformer for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2308.09472/) (2023)
- [Pair then Relation: Pair-Net for Panoptic Scene Graph Generation](https://mlsanity.com/paper/2307.08699/) (2023)
- [Hierarchical Memory Learning for Fine-Grained Scene Graph Generation](https://mlsanity.com/paper/2203.06907/) (2022)
- [Compositional Feature Augmentation for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2308.06712/) (2023)
- [Adaptive Fine-Grained Predicates Learning for Scene Graph Generation](https://mlsanity.com/paper/2207.04602/) (2022)
- [Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2208.03763/) (2022)
- [HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation](https://mlsanity.com/paper/2403.12033/) (2024)
- [A Causal Adjustment Module for Debiasing Scene Graph Generation](https://mlsanity.com/paper/2503.17862/) (2025)
- [Unified Visual Relationship Detection with Vision and Language Models](https://mlsanity.com/paper/2303.08998/) (2023)
- [FairGen: Towards Fair Graph Generation](https://mlsanity.com/paper/2303.17743/) (2023)
- [VLPrompt: Vision-Language Prompting for Panoptic Scene Graph Generation](https://mlsanity.com/paper/2311.16492/) (2023)
- [Predicate correlation learning for scene graph generation](https://mlsanity.com/paper/2107.02713/) (2021)
- [Universal Scene Graph Generation](https://mlsanity.com/paper/2503.15005/) (2025)
- [Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2207.07913/) (2022)
- [Towards Complex-query Referring Image Segmentation: A Novel Benchmark](https://mlsanity.com/paper/2309.17205/) (2023)
- [Rethinking the Evaluation of Unbiased Scene Graph Generation](https://mlsanity.com/paper/2208.01909/) (2022)

## Abstract (arXiv)

Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world scenarios. Conventional debiasing research mainly studies from the view of balancing data distribution or learning unbiased models and representations, ignoring the correlations among the biased classes. In this work, we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the relationships, making the tail relationships receive more attention in a coarse-to-fine mode. To this end, we propose a novel debiasing Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model. The CogTree distinguishes remarkably different relationships at first and then focuses on a small portion of easily confused ones. Then, we propose a debiasing loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships. The loss is model-agnostic and consistently boosting the performance of several state-of-the-art models. The code is available at: https://github.com/CYVincent/Scene-Graph-Transformer-CogTree.