# Inter-object Discriminative Graph Modeling for Indoor Scene Recognition

arXiv: https://arxiv.org/abs/2311.05919 · HTML: https://mlsanity.com/paper/2311.05919/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [Semantic-Aware Scene Recognition](https://mlsanity.com/paper/1909.02410/) (2019)
- [Scene Recognition with Prototype-agnostic Scene Layout](https://mlsanity.com/paper/1909.03234/) (2019)
- [Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition](https://mlsanity.com/paper/2108.00399/) (2021)
- [DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments](https://mlsanity.com/paper/1908.00191/) (2019)
- [BORM: Bayesian Object Relation Model for Indoor Scene Recognition](https://mlsanity.com/paper/2108.00397/) (2021)
- [Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings](https://mlsanity.com/paper/1908.06422/) (2019)
- [Content and Context Features for Scene Image Representation](https://mlsanity.com/paper/2006.03217/) (2020)
- [Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification](https://mlsanity.com/paper/2301.00145/) (2022)
- [SRRM: Semantic Region Relation Model for Indoor Scene Recognition](https://mlsanity.com/paper/2305.08540/) (2023)
- [From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition](https://mlsanity.com/paper/1807.08624/) (2018)

## Built on by (most cited first, 2 of 2)

- [Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI](https://mlsanity.com/paper/2510.04978/) (2025)
- [PRISM-CAFO: Prior-conditioned Remote-sensing Infrastructure Segmentation and Mapping for CAFOs](https://mlsanity.com/paper/2601.11451/) (2026)

## Abstract (arXiv)

Variable scene layouts and coexisting objects across scenes make indoor scene recognition still a challenging task. Leveraging object information within scenes to enhance the distinguishability of feature representations has emerged as a key approach in this domain. Currently, most object-assisted methods use a separate branch to process object information, combining object and scene features heuristically. However, few of them pay attention to interpretably handle the hidden discriminative knowledge within object information. In this paper, we propose to leverage discriminative object knowledge to enhance scene feature representations. Initially, we capture the object-scene discriminative relationships from a probabilistic perspective, which are transformed into an Inter-Object Discriminative Prototype (IODP). Given the abundant prior knowledge from IODP, we subsequently construct a Discriminative Graph Network (DGN), in which pixel-level scene features are defined as nodes and the discriminative relationships between node features are encoded as edges. DGN aims to incorporate inter-object discriminative knowledge into the image representation through graph convolution and mapping operations (GCN). With the proposed IODP and DGN, we obtain state-of-the-art results on several widely used scene datasets, demonstrating the effectiveness of the proposed approach.