Measuring Faithful and Plausible Visual Grounding in VQA (2023)

Abstract: Metrics for Visual Grounding (VG) in Visual Question Answering (VQA) systems primarily aim to measure a system’s reliance on relevant parts of the image when inferring an answer to the given question. Lack of VG has been a common problem among state-of-the-art VQA systems and can manifest in over-reliance on irrelevant image parts or a disregard for the visual modality entirely. Although inference capabilities of VQA models are often illustrated by a few qualitative illustrations, most systems are not quantitatively assessed for their VG properties. We believe, an easily calculated criterion for meaningfully measuring a system’s VG can help remedy this shortcoming, as well as add another valuable dimension to model evaluations and analysis. To this end, we propose a new VG metric that captures if a model a) identifies question-relevant objects in the scene, and b) actually relies on the information contained in the relevant objects when producing its answer, i.e., if its visual grounding is both “faithful” and “plausible”. Our metric, called Faithful & Plausible Visual Grounding (FPVG), is straightforward to determine for most VQA model designs. We give a detailed description of FPVG and evaluate several reference systems spanning various VQA architectures. Code to support the metric calculations on the GQA data set is available on GitHub.

## introduction visual question answering (vqa) is the task of answering natural language questions about image contents. visual grounding (vg) in vqa measures a vqa system's inherent proclivity to base its inference on image regions referenced in the given question and relevant to the answer. a wellgrounded system infers an answer to a given question by relying on image regions relevant to the question and plausible to humans. hence, visually grounded inference in vqa can be broken down into two aspects: (1) image contents impact the inference process, and (2) inference is based on relevant image contents. evidence of problematic behavior that arises from a lack of (1) includes an over-reliance on language priors @xcite @xcite , while a lack of (2) can cause models to react to changes in irrelevant parts of the image @xcite . both characteristics can hurt a model's capacity to provide consistent and reliable performances. metrics that quantify a model's vg characteristics aim to capture its internal reasoning process based on methods of model explanation. these explanations generally vary in properties of plausibility and faithfulness. plausible explanations of a model's behavior prioritize human interpretability, e.g., by illustrating a clear inference path over relevant objects that lead to the decision, but might not accurately reflect a model's actual decision-making process. faithful explanations, on the other hand, prioritize a more accurate reflection of a model's decision-making process, possibly at the expense of human interpretability. examples of plausible explanation methods are attention mechanisms @xcite over visual input objects, and multi-task objectives that learn to produce inference paths without conclusive involvement in the main model's answer decision @xcite . faithful explanation methods may employ testing schemes with modulated visual inputs followed by comparisons of the model's output behavior across test runs @xcite . while the latter types of metrics are particularly suited for the use-case of object-based visual input in vqa, they often a) require large compute budgets to evaluate the required number of input permutations (e.g. swapmix @xcite , leave-one-out @xcite ); b) might evaluate in unnecessary depth, like in the case of softmax-score-based evaluations @xcite ; and/or c) evaluate individual properties separately and without considering classification contexts, thereby missing the full picture @xcite , see also §3.4). in this work, we propose a vg metric that is both faithful and plausible in its explanations. faithful & plausible visual grounding (fpvg) quantifies a model's faithful reliance on plausibly relevant image regions (fig. 1 ). fpvg is based on a model's answering behavior for modulated sets of image input regions, similar to other faithfulness metrics (in particular deyoung et al. ( 2020 )), while avoiding their above-mentioned shortcomings (details in §3.4). to determine the state-of-the-art for vg in vqa, we use fpvg to measure various representative vqa methods ranging from onestep and multi-hop attention-based methods, over transformer-based models with and without crossmodality pre-training, to (neuro-)symbolic methods. we conclude this work with investigations into the importance of vg for vqa generalization research (represented by out-of-distribution (ood) testing), thereby further establishing the value of fpvg as an analytical tool. the gqa data set @xcite for compositional vqa is particularly suited for our tasks, as it provides detailed inference and grounding information for the majority of its questions. contributions. summarized as follows: visual grounding" (fpvg) for quantification of plausible & faithful vg in vqa. ## related work various metrics have been proposed to measure vg in vqa models. we roughly group these into direct and indirect methods. 1) direct methods: the most widely used methods measuring the importance of image regions to a given question are based on a model's attention mechanisms @xcite , or use gradient-based sensitivities (in particular variants of gradcam @xcite ). vg is then estimated, e.g., by accumulating importance scores over matching and relevant annotated image regions @xcite , or by some form of rank correlation @xcite . aside from being inapplicable to non-attention-based vqa models (e.g., symbolic methods like @xcite ; @xcite ), attention scores have the disadvantage of becoming harder to interpret the more attention layers are employed for various tasks in a model. this gets more problematic in complex transformerbased models that have a multitude of attention layers over the input image (oscar @xcite , lxmert @xcite , mcan @xcite , mmn @xcite ). additionally, attention mechanisms have been a topic of debate regarding the faithfulness of their explanation @xcite . gradient-based sensitivity scores can theoretically produce faithful explanations, but require a careful choice of technique and implementation for each model individually to achieve meaningful measurements in practice @xcite . various works introduce their own vg metric based on attention measurements (e.g., gqa-grounding @xcite , vlr @xcite , mac-caps @xcite ) or gradcam-based feature sensitivities @xcite @xcite . 2) indirect methods: these include methods that measure vg based on a model's predictions under particular test (and train) conditions, e.g., with perturbations of image features @xcite @xcite @xcite , or specially designed out-of-distribution test sets that can inform us about a model's insufficient vg properties @xcite @xcite . fpvg is related to deyoung et al. (2020) in particular and uses perturbations of image features to approximate a direct measurement of vg w.r.t. relevant objects in the input image. thus, we categorize fpvg as an "indirect" vg evaluation method. finally, we note that vg can be considered a sub-problem of the vqa desiderata gathered under the term "right for right reasons" (rrr) @xcite . rrr may additionally include investigations of causal behavior in a model that goes beyond (and may not be strictly dependent on) vg and may involve probing the model for its robustness and consistency in explanations, e.g., via additional (follow-up) questions @xcite @xcite . ## conclusion we introduced faithful & plausible visual grounding (fpvg), a metric that facilitates and streamlines the analysis of vg in vqa systems. using fpvg, we investigated vqa systems of various architectural designs and found that many models struggle to reach the level of faithful & plausible vg that systems based on symbolic inference can provide. finally, we have shown that fpvg can be a valuable tool in analyzing vqa system behavior, as exemplified by investigations of the vg-ood relationship. here, we found that vg plays an important role in ood scenarios, where, compared to id scenarios, bad vg leads to considerably more errors than good vg, thus providing us with a compelling argument for pursuing better-grounded models.