RA3: A Human-in-the-loop Framework for Interpreting and Improving Image Captioning with Relation-Aware Attribution Analysis

Interpreting model behavior is crucial for model evaluation and optimization. Recent research demonstrates that incorporating human intelligence into the learning process effectively improve the interpretability and performance of the machine learning models, especially for simple classification tasks. However, the image captioning task has not received much attention. Such complex sequential tasks generally contain semantic relationships between different concepts, which pose challenges for interpreting model behavior and developing optimization methods. In this paper, we present RA
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(Relation-Aware Attribution Analysis), a human-in-the-loop framework, for improving the interpretability, and further boosting the performance of the image captioning model. Specifically, we first engage human participants in two types of annotation tasks to identify what the model actually focuses on (model attribution) and what it should focus on (human rationale) at the conceptual level, supported by machine learning interpretability methods. Then, we identify and filter hard instances based on relation-aware model attribution for both validating the quality of the explanation and eliminating low-quality captions (this process is also considered as a kind of data debugging). We subsequently designed an explanation loss that penalizes the difference between model attribution and human rationale to optimize the model's behavior for improving caption quality. Through extensive experiments on crowdsourced annotations and MSCOCO, the experiment results indicate that the explanations produced by RA
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can accurately describe the model's behavior, effectively identify difficult instances, and significantly improve the caption quality.