Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning (2212.00259v2)

Abstract: Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization. Due to the multi-modal nature of this task, multiple factors of variation are intertwined, making generalization difficult to analyze. This motivates us to introduce a virtual benchmark, Super-CLEVR, where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality. With controllably generated data, Super-CLEVR enables us to test VQA methods in situations where the test data differs from the training data along each of these axes. We study four existing methods, including two neural symbolic methods NSCL and NSVQA, and two non-symbolic methods FiLM and mDETR; and our proposed method, probabilistic NSVQA (P-NSVQA), which extends NSVQA with uncertainty reasoning. P-NSVQA outperforms other methods on three of the four domain shift factors. Our results suggest that disentangling reasoning and perception, combined with probabilistic uncertainty, form a strong VQA model that is more robust to domain shifts. The dataset and code are released at https://github.com/Lizw14/Super-CLEVR.

An Analysis of Super-CLEVR: Evaluating Domain Robustness in Visual Reasoning

The paper introduces Super-CLEVR, an innovative virtual benchmark designed to evaluate domain robustness in Visual Question Answering (VQA) tasks. Visual question answering models are known to exhibit suboptimal performance on out-of-distribution (OOD) data, and struggle with domain generalization due, in part, to the complex feature interactions inherent in multi-modal inputs. This work aims to systematically dissect domain shifts in VQA by isolating distinct factors and examining their impact on model performance independently.

Motivation and Approach

The authors identify four primary factors contributing to domain shifts in VQA tasks: visual complexity, question redundancy, concept distribution, and concept compositionality. Super-CLEVR allows researchers to modify these factors separately, offering controlled environments in which to evaluate VQA models rigorously.

1. Visual Complexity: Referring to the nature and interaction of visual components, visual complexity can affect the model's ability to interpret scenes. The dataset varies this by implementing progressively complex visual scenes.
2. Question Redundancy: Redundancy in VQA often involves including superfluous information in questions due to either over-specified attributes or relationships. Super-CLEVR modifies redundancy by generating questions with varying levels of extraneous detail.
3. Concept Distribution: This factor considers the frequency and variety of concepts (e.g., objects or attributes) during training versus testing. Unbalanced concept distributions create biases that hinder model performance. The dataset variations emulate balanced and long-tailed distributions to assess robustness.
4. Concept Compositionality: This assesses the degree of co-occurrence and interaction among concepts, examining whether a model trained on typical combinations can adapt to atypical pairings.

Dataset and Methodology

Super-CLEVR is generated by replacing simple shapes in CLEVR with more complex 3D vehicle models, including diverse attributes and part annotations. Scene complexity and domain shift factors are independently controlled through these augmented graphics.

The authors benchmark several models (e.g., FiLM, mDETR, NSCL, NSVQA) and introduce P-NSVQA, an extended NSVQA model incorporating probabilistic reasoning to account for uncertainty in visual parsing. Probabilistic NSVQA is demonstrated to outperform its deterministic counterpart, showcasing robustness in handling domain shifts.

Results

The evaluation yields several insights:

• Performance Analysis: All models face a decline in performance under domain shifts, with P-NSVQA showing the least deterioration, especially concerning question redundancy and concept distribution.
• Robustness Across Factors: Modular symbolic models, particularly those integrating disentangled reasoning and perception, performed better under variations in question redundancy and concept distribution.
• Probabilistic Reasoning: Incorporating uncertainty into symbolic reasoning (P-NSVQA) provides notable improvement and robustness, hinting at future directions in VQA model architecture.

Implications and Future Research

The practical implications underscore the significance of developing VQA models with enhanced domain robustness, critical for real-world application in environments with variable data distributions. This paper also suggests the potential of hybrid models employing probabilistic reasoning to bolster domain adaptability.

For theoretical exploration, this controlled setting provides pathways to dissect and understand the nuanced failures of current models under OOD testing. Future work might delve into integrating such controlled benchmarks with real-world datasets to ensure comprehensive coverage of domain variability and model generalization capacities.

Ultimately, Super-CLEVR emerges as a significant tool in diagnosing and advancing the robustness of VQA and similar AI tasks, laying groundwork for subsequent research targeting more generalized intelligence across diverse data ecosystems.