# Unbiased Directed Object Attention Graph for Object Navigation

arXiv: https://arxiv.org/abs/2204.04421 · HTML: https://mlsanity.com/paper/2204.04421/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 9 Apr 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments](https://mlsanity.com/paper/1904.10151/) (2019)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [RoboTHOR: An Open Simulation-to-Real Embodied AI Platform](https://mlsanity.com/paper/2004.06799/) (2020)
- [RUBi: Reducing Unimodal Biases in Visual Question Answering](https://mlsanity.com/paper/1906.10169/) (2019)
- [REPAIR: Removing Representation Bias by Dataset Resampling](https://mlsanity.com/paper/1904.07911/) (2019)
- [Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2104.00308/) (2021)
- [Visual Semantic Navigation using Scene Priors](https://mlsanity.com/paper/1810.06543/) (2018)
- [SOON: Scenario Oriented Object Navigation with Graph-based Exploration](https://mlsanity.com/paper/2103.17138/) (2021)
- [Building Generalizable Agents with a Realistic and Rich 3D Environment](https://mlsanity.com/paper/1801.02209/) (2018)
- [Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning](https://mlsanity.com/paper/1812.00971/) (2018)
- [Auxiliary Tasks and Exploration Enable ObjectNav](https://mlsanity.com/paper/2104.04112/) (2021)
- [Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels](https://mlsanity.com/paper/1512.06974/) (2015)
- [Learning Object Relation Graph and Tentative Policy for Visual Navigation](https://mlsanity.com/paper/2007.11018/) (2020)
- [SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2110.14143/) (2021)
- [Hierarchical Object-to-Zone Graph for Object Navigation](https://mlsanity.com/paper/2109.02066/) (2021)

## Built on by (most cited first, 18 of 18)

- [Vision-and-Language Navigation via Causal Learning](https://mlsanity.com/paper/2404.10241/) (2024)
- [Search for or Navigate to? Dual Adaptive Thinking for Object Navigation](https://mlsanity.com/paper/2208.00553/) (2022)
- [A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation](https://mlsanity.com/paper/2305.03602/) (2023)
- [Graph based Environment Representation for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2301.04352/) (2023)
- [Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation](https://mlsanity.com/paper/2302.01520/) (2023)
- [PASTS: Progress-Aware Spatio-Temporal Transformer Speaker For Vision-and-Language Navigation](https://mlsanity.com/paper/2305.11918/) (2023)
- [MAGIC: Meta-Ability Guided Interactive Chain-of-Distillation for Effective-and-Efficient Vision-and-Language Navigation](https://mlsanity.com/paper/2406.17960/) (2024)
- [Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidance](https://mlsanity.com/paper/2308.12845/) (2023)
- [Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation](https://mlsanity.com/paper/2312.03327/) (2023)
- [SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas](https://mlsanity.com/paper/2510.19766/) (2025)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2403.03405/) (2024)
- [Temporal-Spatial Object Relations Modeling for Vision-and-Language Navigation](https://mlsanity.com/paper/2403.15691/) (2024)
- [Leveraging Unknown Objects to Construct Labeled-Unlabeled Meta-Relationships for Zero-Shot Object Navigation](https://mlsanity.com/paper/2405.15222/) (2024)
- [Efficient Strategy Learning by Decoupling Searching and Pathfinding for Object Navigation](https://mlsanity.com/paper/2406.14103/) (2024)
- [Recursive Visual Imagination and Adaptive Linguistic Grounding for Vision Language Navigation](https://mlsanity.com/paper/2507.21450/) (2025)
- [Enhancing Vision-Language Navigation with Multimodal Event Knowledge from Real-World Indoor Tour Videos](https://mlsanity.com/paper/2602.23937/) (2026)
- [Graph-MambaNav: Spatial-Temporal Graph Mamba Leveraging Object-Relation Knowledge for Object-Goal Navigation](https://mlsanity.com/paper/2608.13723/) (2026)

## Abstract (arXiv)

Object navigation tasks require agents to locate specific objects in unknown environments based on visual information. Previously, graph convolutions were used to implicitly explore the relationships between objects. However, due to differences in visibility among objects, it is easy to generate biases in object attention. Thus, in this paper, we propose a directed object attention (DOA) graph to guide the agent in explicitly learning the attention relationships between objects, thereby reducing the object attention bias. In particular, we use the DOA graph to perform unbiased adaptive object attention (UAOA) on the object features and unbiased adaptive image attention (UAIA) on the raw images, respectively. To distinguish features in different branches, a concise adaptive branch energy distribution (ABED) method is proposed. We assess our methods on the AI2-Thor dataset. Compared with the state-of-the-art (SOTA) method, our method reports 7.4%, 8.1% and 17.6% increase in success rate (SR), success weighted by path length (SPL) and success weighted by action efficiency (SAE), respectively.