Panoptic Scene Graph Generation with Semantics-Prototype Learning
Proposes ADTrans framework using semantic prototypes to address bias in PSG, improving R@100 by 3-4% and balancing long-tail relations.
Key Findings
Methodology
This paper introduces ADTrans, an adaptive bias transfer framework leveraging relation semantic prototypes, invariance measurement, and contrastive learning. It extracts relation representations, measures their invariance, and dynamically updates prototypes to identify and transfer biased annotations to informative, unified labels. Multi-stage filtering removes noisy data, ensuring high-quality relation embeddings. The approach integrates relation representation learning, prototype-based bias identification, and transfer mechanisms, significantly reducing bias effects and improving relation classification, especially for long-tail categories.
Key Results
- On VG and PSG datasets, ADTrans improves R@100 by 3-4 points, with mean recall (mR@100) increasing over 10%. It achieves state-of-the-art results in scene graph detection (SGDet), especially excelling in tail relation categories, demonstrating better relation balance and generalization.
- In relation classification tasks, models with ADTrans outperform baselines like Motifs and VCTree across metrics, showing enhanced robustness and bias mitigation. Ablation studies confirm the importance of invariance measurement, prototype updates, and multi-stage filtering for performance gains.
- The method effectively filters biased samples, leading to more consistent relation annotations, which in turn improves downstream tasks such as image captioning and visual question answering, indicating broad applicability.
Significance
This work addresses a fundamental challenge in scene graph generation—bias in relation annotations—by proposing a systematic, prototype-based bias correction framework. It advances the field by enabling models to learn more accurate, unbiased relation representations, especially for underrepresented classes. The approach enhances the interpretability and robustness of scene understanding systems, with potential impacts on autonomous driving, robotics, and multimodal AI applications. Its generalizable bias correction mechanism offers a new paradigm for improving large-scale vision-language models, reducing reliance on costly manual annotations.
Technical Contribution
The paper introduces a novel relation semantic prototype learning framework that combines invariance measurement, contrastive learning, and dynamic prototype updating. It innovatively integrates multi-stage bias filtering and adaptive transfer strategies, enabling models to automatically identify and correct biased annotations. The approach is compatible with various scene graph models, significantly boosting their performance on long-tail relations and improving overall robustness. Theoretical guarantees on relation representation invariance and empirical validation demonstrate its effectiveness, setting new benchmarks in PSG and VG relation detection.
Novelty
This is the first work to leverage relation semantic prototypes for bias transfer in scene graph generation, integrating invariance measurement and dynamic prototype updates. Unlike prior methods relying solely on data augmentation or regularization, this approach systematically identifies and transfers biased annotations, achieving a more balanced and accurate relation understanding. Its combination of multi-stage filtering, contrastive learning, and prototype-based transfer constitutes a significant innovation in bias correction for vision-language tasks.
Limitations
- The method depends heavily on the quality of pre-trained relation models; if these models are biased, the bias correction may be less effective.
- Multi-stage filtering introduces additional computational overhead, which may hinder real-time applications on large datasets.
- Current focus is primarily on relation categories; spatial and attribute biases are less addressed, requiring further extension.
Future Work
Future directions include integrating multi-modal cues such as textual descriptions and depth information to enhance bias detection accuracy. Developing end-to-end training pipelines to reduce complexity and computational costs is also planned. Extending bias correction to object attributes and spatial relations, as well as exploring unsupervised or weakly supervised approaches, will further broaden the applicability of the framework.
AI Executive Summary
Scene understanding is a cornerstone of advanced computer vision, enabling machines to interpret complex environments. Scene graph generation (SGG) plays a vital role by translating visual scenes into structured semantic representations. However, existing models struggle with biased annotations stemming from annotator preferences and semantic overlaps among predicates, leading to poor generalization, especially for long-tail relations. These biases hinder the deployment of scene understanding systems in real-world applications like autonomous driving and robotics.
This paper introduces ADTrans, a novel framework that leverages relation semantic prototypes to address bias in PSG. The core idea involves measuring the invariance of relation representations, dynamically updating prototypes, and filtering biased samples through a multi-stage process. By transferring biased annotations to more informative, standardized labels, ADTrans enhances the quality and consistency of training data. The approach combines contrastive learning, invariance regularization, and adaptive bias transfer, resulting in more balanced relation representations.
Experimental results on VG and PSG datasets demonstrate that ADTrans significantly outperforms baseline models, improving R@100 by 3-4 points and boosting mean recall by over 10%. The method excels in recognizing long-tail relations, achieving state-of-the-art performance in scene graph detection and classification tasks. These improvements translate into more accurate scene understanding, with broad implications for downstream vision-language tasks.
Overall, ADTrans offers a systematic, scalable solution to bias in scene graph generation, fostering more robust and unbiased models. Its ability to automatically identify and correct biased annotations paves the way for more reliable AI systems in complex real-world scenarios. Future work will focus on integrating multi-modal cues and extending bias correction to other attributes, aiming for comprehensive scene understanding in diverse environments.
Deep Analysis
Background
Scene graph generation (SGG) has evolved from simple object detection and predicate classification to more comprehensive scene understanding using panoptic segmentation. Early models like Xu et al.'s IMP and Zellers et al.'s Motifs laid the groundwork but faced challenges with biased annotations and long-tail distributions. Recent advances incorporate contextual reasoning and multi-modal data, yet biases from annotator preferences and semantic overlaps persist, limiting model robustness. Addressing these biases is critical for deploying reliable scene understanding systems in real-world applications such as autonomous vehicles and robotics, where accurate relation detection is essential.
Core Problem
Despite progress, current PSG models suffer from biased and noisy annotations, mainly due to inconsistent labeling practices and semantic overlaps among predicates. These biases cause models to overfit frequent relations and underperform on rare ones, exacerbating the long-tail problem. The core challenge is to automatically identify biased annotations and transfer them to more accurate, informative labels, ensuring the model learns a balanced and semantically consistent relation space. Achieving this requires a mechanism to measure relation invariance, detect bias, and adaptively correct labels without manual intervention.
Innovation
The paper introduces a relation semantics-prototype learning framework that uniquely combines invariance measurement, contrastive learning, and dynamic prototype updating. It innovatively employs a multi-stage bias filtering process to identify and transfer biased annotations, ensuring relation representations are invariant and discriminative. Unlike prior works limited to data augmentation or regularization, this approach systematically aligns relation embeddings with semantic prototypes, significantly improving the recognition of tail relations. Its adaptive transfer mechanism enhances model robustness and generalization, representing a new paradigm in bias correction for scene graph generation.
Methodology
- �� Relation representation extraction: Use pre-trained models like VCTree to generate initial relation embeddings.
- �� Invariance measurement: Calculate the variance of relation loss across samples within each class to assess stability.
- �� Contrastive relation embedding: Employ InfoNCE loss with angular margin to optimize semantic similarity and dissimilarity among relation pairs.
- �� Visual-text alignment: Incorporate confusion matrices from visual models to align textual relation embeddings with visual similarity.
- �� Invariant representation regularization: Minimize variance of relation loss to promote unbiased features.
- �� Semantic prototype learning: Maintain and update relation prototypes dynamically using moving averages based on invariance scores.
- �� Multi-stage bias filtering: Use variance and distribution shift metrics to identify and remove biased/noisy samples.
- �� Adaptive data transfer: Calculate similarity scores between prototypes to transfer biased annotations to standardized labels, ensuring relation consistency.
Experiments
Experiments conducted on VG and PSG datasets involve training baseline models like Motifs, VCTree, and PSGTR, with and without ADTrans. Metrics include R@K, mR@K, and PR, evaluated across scene graph detection and classification tasks. Hyperparameters such as temperature T=0.05, prototype update β=5e-5, and filtering threshold D=50 are tuned. Ablation studies isolate the impact of invariance measurement, prototype updates, and filtering. Results show that ADTrans improves R@100 by 3-4 points, with over 10% increase in mR@100, especially benefiting tail classes, confirming its effectiveness in bias mitigation and relation recognition.
Results
ADTrans achieves state-of-the-art performance on VG datasets, with R@100 reaching 36.3% in SGDet, outperforming baseline models by 3-4%. The mean recall (mR@100) improves by over 10%, indicating better tail relation recognition. Ablation studies reveal that invariance regularization and dynamic prototype updates contribute significantly to performance gains. The method also balances relation distribution, reducing bias effects and improving downstream task accuracy, such as image captioning and VQA, demonstrating broad applicability.
Applications
This framework can be directly applied in autonomous driving to improve scene understanding, in robotics for better environment perception, and in multimedia analysis for more accurate relation extraction. Its ability to automatically correct biased annotations reduces manual labeling efforts and enhances model robustness in diverse environments. Long-term, integrating multi-modal cues and expanding bias correction to object attributes could further advance comprehensive scene understanding, enabling more reliable AI systems across industries.
Limitations & Outlook
Dependence on pre-trained relation models may limit effectiveness if these models are biased. The multi-stage filtering process introduces additional computational overhead, affecting scalability. The current focus is primarily on relation categories, with less emphasis on spatial or attribute biases. Future work should address efficiency, extend bias correction to other attributes, and explore unsupervised approaches to reduce reliance on pre-trained models.
Plain Language Accessible to non-experts
想象你在整理一个大相册，里面有很多照片，每张都标着一些描述，比如“有人在跑”或“有人在走”。不同的人可能用不同的词描述同一件事，比如“跑”和“快跑”。这就像在训练一个机器人，让它学会理解这些动作，但标签不一致让它变得困惑。科学家们设计了一种方法，把所有类似的描述归到一个“标准标签”里，就像给“跑”贴上一个标签，让机器人知道“跑”到底是什么。这样，不管别人怎么描述，机器人都能理解是一样的。这个方法还能自动发现那些不准确的标签，把它们改正过来，让机器人学得更聪明。最终，机器人能更准确地理解场景，就像我们一眼看出照片里发生了什么一样。
ELI14 Explained like you're 14
想象你在整理一堆照片，每张照片都写着一些描述，比如“有人在跑”或者“有人在走”。可是，有时候不同的人会用不同的词来描述同一件事，比如“跑”和“快跑”。这就像每个人用不同的语言说同一件事，让机器人难以理解。科学家们想让机器人学会更聪明，不会被这些不同的描述迷惑。他们设计了一种方法，把所有类似的描述都归到一个“原型”里，就像把所有的“跑”都放在一个篮子里。这样，无论别人怎么描述，机器人都能知道它们其实是一样的。更棒的是，这个方法还能发现那些描述不准确或者偏差的照片，把它们改正过来。这样，机器人学会了更好地理解场景，就像我们看一张照片一眼就知道发生了什么一样。
Abstract
Panoptic Scene Graph Generation (PSG) parses objects and predicts their relationships (predicate) to connect human language and visual scenes. However, different language preferences of annotators and semantic overlaps between predicates lead to biased predicate annotations in the dataset, i.e. different predicates for same object pairs. Biased predicate annotations make PSG models struggle in constructing a clear decision plane among predicates, which greatly hinders the real application of PSG models. To address the intrinsic bias above, we propose a novel framework named ADTrans to adaptively transfer biased predicate annotations to informative and unified ones. To promise consistency and accuracy during the transfer process, we propose to measure the invariance of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities. Meanwhile, we continuously measure the distribution changes between each presentation and its prototype, and constantly screen potential biased data. Finally, with the unbiased predicate-prototype representation embedding space, biased annotations are easily identified. Experiments show that ADTrans significantly improves the performance of benchmark models, achieving a new state-of-the-art performance, and shows great generalization and effectiveness on multiple datasets.