# Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation

arXiv: https://arxiv.org/abs/2006.07585 · HTML: https://mlsanity.com/paper/2006.07585/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

## Built on by (most cited first, 22 of 22)

- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [Scene Graph Generation: A Comprehensive Survey](https://mlsanity.com/paper/2201.00443/) (2022)
- [Recovering the Unbiased Scene Graphs from the Biased Ones](https://mlsanity.com/paper/2107.02112/) (2021)
- [Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning](https://mlsanity.com/paper/2208.08165/) (2022)
- [Exploiting Scene Graphs for Human-Object Interaction Detection](https://mlsanity.com/paper/2108.08584/) (2021)
- [Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection](https://mlsanity.com/paper/2311.01755/) (2023)
- [HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation](https://mlsanity.com/paper/2403.12033/) (2024)
- [Semantic Compositional Learning for Low-shot Scene Graph Generation](https://mlsanity.com/paper/2108.08600/) (2021)
- [VQArt-Bench: A semantically rich VQA Benchmark for Art and Cultural Heritage](https://mlsanity.com/paper/2510.12750/) (2025)
- [Towards Heterogeneous Long-tailed Learning: Benchmarking, Metrics, and Toolbox](https://mlsanity.com/paper/2307.08235/) (2023)
- [Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning](https://mlsanity.com/paper/2401.14626/) (2024)
- [ReCon1M:A Large-scale Benchmark Dataset for Relation Comprehension in Remote Sensing Imagery](https://mlsanity.com/paper/2406.06028/) (2024)
- [RepSGG: Novel Representations of Entities and Relationships for Scene Graph Generation](https://mlsanity.com/paper/2309.03240/) (2023)
- [Unbiased Scene Graph Generation using Predicate Similarities](https://mlsanity.com/paper/2210.00920/) (2022)
- [ScriptHOI: Learning Scripted State Transitions for Open-Vocabulary Human-Object Interaction Detection](https://mlsanity.com/paper/2605.05057/) (2026)
- [HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities](https://mlsanity.com/paper/2605.06157/) (2026)
- [Can We Build Scene Graphs, Not Classify Them? FlowSG: Progressive Image-Conditioned Scene Graph Generation with Flow Matching](https://mlsanity.com/paper/2604.18623/) (2026)
- [CAGE-SGG: Counterfactual Active Graph Evidence for Open-Vocabulary Scene Graph Generation](https://mlsanity.com/paper/2604.22274/) (2026)
- [ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation](https://mlsanity.com/paper/2604.22546/) (2026)
- [RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses](https://mlsanity.com/paper/2605.20823/) (2026)
- [Resolving Ambiguity in Composed Image Retrieval via Calibrated Interaction](https://mlsanity.com/paper/2605.24634/) (2026)
- [Show, Don't Ask: Generative Visual Disambiguation for Composed Image Retrieval with Turn-Valid Coverage](https://mlsanity.com/paper/2606.18992/) (2026)

## Abstract (arXiv)

Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue. Existing methods largely rely on either external knowledge or statistical bias information to alleviate this problem. In this paper, we tackle this issue from another two aspects: (1) scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism; and (2) long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail. Extensive experiments on the benchmark dataset Visual Genome on three tasks demonstrate that our method outperforms current state-of-the-art competitors.