# ALF: Adaptive Label Finetuning for Scene Graph Generation

arXiv: https://arxiv.org/abs/2312.17425 · HTML: https://mlsanity.com/paper/2312.17425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 29 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [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)
- [Learning to Compose Dynamic Tree Structures for Visual Contexts](https://mlsanity.com/paper/1812.01880/) (2018)
- [Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2104.00308/) (2021)
- [SGTR: End-to-end Scene Graph Generation with Transformer](https://mlsanity.com/paper/2112.12970/) (2021)
- [Fully Convolutional Scene Graph Generation](https://mlsanity.com/paper/2103.16083/) (2021)
- [Fine-Grained Scene Graph Generation with Data Transfer](https://mlsanity.com/paper/2203.11654/) (2022)
- [Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning](https://mlsanity.com/paper/2208.08165/) (2022)
- [Panoptic Scene Graph Generation with Semantics-Prototype Learning](https://mlsanity.com/paper/2307.15567/) (2023)
- [Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World](https://mlsanity.com/paper/2303.13233/) (2023)
- [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)
- [Vision Relation Transformer for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2308.09472/) (2023)
- [TextPSG: Panoptic Scene Graph Generation from Textual Descriptions](https://mlsanity.com/paper/2310.07056/) (2023)

## Abstract (arXiv)

Scene Graph Generation (SGG) endeavors to predict the relationships between subjects and objects in a given image. Nevertheless, the long-tail distribution of relations often leads to biased prediction on coarse labels, presenting a substantial hurdle in SGG. To address this issue, researchers focus on unbiased SGG and introduce data transfer methods to transfer coarse-grained predicates into fine-grained ones across the entire dataset. However, these methods encounter two primary challenges: 1) They overlook the inherent context constraints imposed by subject-object pairs, leading to erroneous relations transfer. 2) Additional retraining process are required after the data transfer, which incurs substantial computational costs. To overcome these limitations, we introduce the first plug-and-play one-stage data transfer pipeline in SGG, termed Adaptive Label Finetuning (ALF), which eliminates the need for extra retraining sessions and meanwhile significantly enhance models' relation recognition capability across various SGG benchmark approaches. Specifically, ALF consists of two components: Adaptive Label Construction (ALC) and Adaptive Iterative Learning (AIL). By imposing Predicate-Context Constraints within relation space, ALC adaptively re-ranks and selects candidate relations in reference to model's predictive logits utilizing the Restriction-Based Judgment techniques, achieving robust relation transfer. Supervised with labels transferred by ALC, AIL iteratively finetunes the SGG models in an auto-regressive manner, which mitigates the substantial computational costs arising from the retraining process. Extensive experiments demonstrate that ALF achieves a 16% improvement in mR@100 compared to the typical SGG method Motif, with only a 6% increase in calculation costs compared to the state-of-the-art method IETrans.