Unbiased Scene Graph Generation from Biased Training (2002.11949v3)

Abstract: Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e.g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach". Given such SGG, the down-stream tasks such as VQA can hardly infer better scene structures than merely a bag of objects. However, debiasing in SGG is not trivial because traditional debiasing methods cannot distinguish between the good and bad bias, e.g., good context prior (e.g., "person read book" rather than "eat") and bad long-tailed bias (e.g., "near" dominating "behind / in front of"). In this paper, we present a novel SGG framework based on causal inference but not the conventional likelihood. We first build a causal graph for SGG, and perform traditional biased training with the graph. Then, we propose to draw the counterfactual causality from the trained graph to infer the effect from the bad bias, which should be removed. In particular, we use Total Direct Effect (TDE) as the proposed final predicate score for unbiased SGG. Note that our framework is agnostic to any SGG model and thus can be widely applied in the community who seeks unbiased predictions. By using the proposed Scene Graph Diagnosis toolkit on the SGG benchmark Visual Genome and several prevailing models, we observed significant improvements over the previous state-of-the-art methods.

• The paper introduces a causal graph framework with counterfactual analysis to isolate essential causal effects from biased context in Scene Graph Generation.
• The approach defines Total Direct Effect (TDE) for unbiased predicate scoring and demonstrates robust improvements on benchmark datasets.
• Its model-agnostic methodology and novel evaluation toolkit provide actionable insights for reducing biases in visual scene understanding.

Unbiased Scene Graph Generation from Biased Training: A Summary

The paper "Unbiased Scene Graph Generation from Biased Training" investigates the complexities of Scene Graph Generation (SGG), a task that seeks to identify objects and their relationships within an image. Despite its potential to contribute to higher-level visual tasks, SGG remains hindered by biases inherent in the training data. This paper proposes a novel framework utilizing causal inference to mitigate these biases and improve the effectiveness of scene graph predictions.

Background

SGG is essential for developing comprehensive visual scene representations that can facilitate tasks such as visual captioning and visual question answering (VQA). However, the current models often collapse diverse relationship representations into generic categories due to the heavy bias in training data. This makes it challenging for downstream tasks to infer nuanced scene structures.

Framework Overview

The authors present a framework based on causal inference, a departure from the typical likelihood-based methodologies. This approach hinges on constructing a causal graph for SGG and employing counterfactual reasoning to disentangle harmful biases while preserving useful context-based information. Specifically, they introduce the Total Direct Effect (TDE) as the metric for unbiased predicate scoring, which aims to segregate the main causal effects from the side effects triggered by biased contexts.

Contributions

1. Causal Graph Construction: The authors construct a causal graph incorporating the relationships between image features, object classifications, and predicate predictions. This visualization aids in understanding and manipulating the causal relationships affecting SGG.
2. Counterfactual Analysis: The TDE framework involves generating counterfactuals—hypothetical scenarios to assess whether predictions change without specific observed content. By doing this, the framework attempts to remove context-induced biases, focusing instead on the essential causal factors.
3. Model Agnosticism: The framework is not restricted to a specific SGG model, making it versatile and broadly applicable to existing models seeking improved accuracy in representing scene graphs.
4. Evaluation Toolkit: A new evaluation suite, the Scene Graph Diagnosis toolkit, is introduced. This includes bias-sensitive metrics and a novel Sentence-to-Graph Retrieval task aimed at achieving a more thorough understanding of model performance.

Results and Implications

The empirical evaluation demonstrates that the proposed framework significantly improves performance on benchmark datasets like Visual Genome, outperforming state-of-the-art methods in tasks like Predicate Classification, Scene Graph Classification, and Scene Graph Detection. By effectively eliminating harmful bias without sacrificing valuable contextual learning, the model introduces a robust, unbiased approach to SGG.

Future Directions

The paper hints at a promising direction for further research. As causal inference is increasingly applied in AI, exploring more sophisticated causal models that incorporate a broader range of variables could enhance the robustness of scene graph predictions. Moreover, integrating this framework with other visual reasoning tasks could lead to more holistic advancements across AI applications.

Conclusion

This research offers valuable insights into addressing bias in SGG, leveraging causal inference to elevate model performance. By systematically reducing bias while preserving context, the work sets a new standard for unbiased scene graph generation. The framework's broad applicability to different models signifies its potential impact, paving the way for future developments in unbiased AI systems.