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REVIEW 3 major objections 5 minor 65 references

ODExAI: A Comprehensive Object Detection Explainable AI Evaluation

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper proposes ODExAI, a three-axis benchmark for explainable AI in object detection, and uses it to show that D-CLOSE is the most faithful explainer, G-CAME the best localized and fastest, and D-RISE moderate on every axis.

desk verdict A useful integration of existing XAI-for-detection metrics, shipped as a tool and a small benchmark, but the empirical support for the 'reliably measures' claim is thin and partly undercut by the paper's own limitations section. read the letter →

arxiv 2504.19249 v1 pith:7XNDWT2X submitted 2025-04-27 cs.CV

Loc Phuc Truong Nguyen , Hung Truong Thanh Nguyen , Hung Cao This is my paper · ORCID
classification cs.CV
keywords explainableAIobjectdetectionevaluationframeworksaliencymapsfaithfulnesslocalizationaccuracycomputationalcomplexityXAIbenchmarking
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

ODExAI is an evaluation framework that scores explainable AI (XAI) methods for object detectors on three dimensions: localization accuracy, faithfulness to the model's behavior, and computational complexity. The paper demonstrates it in a toy experiment with three explanation methods—D-CLOSE, G-CAME, and D-RISE—across two detectors (YOLOX and Faster R-CNN) and two datasets (MS-COCO and PASCAL VOC). The central empirical finding is that no method wins on all axes: D-CLOSE is the most faithful and produces sparse maps but needs over a minute per image, G-CAME localizes best and runs in under a second but is less faithful, and D-RISE is consistently moderate. Because the ranking changes by dimension, the paper argues that task-specific evaluation, rather than a single headline score, is required before choosing an explanation method.

What carries the argument

The central object is the metric grid: five quantitative scores grouped into three dimensions—Pointing Game and Energy-based Pointing Game for localization, Deletion, Insertion and Over-All for faithfulness, and Sparsity plus runtime for complexity. Each saliency map is scored on every axis, producing a table that exposes the trade-offs. The load-bearing identities are the formulas themselves: $\mathrm{PG}$ counts whether the maximum-saliency point lands inside the annotated bounding box, $\mathrm{EBPG}$ is the fraction of saliency energy inside the box, $\mathrm{OA} = \mathrm{AUC}(\text{Insertion}) - \mathrm{AUC}(\text{Deletion})$ combines the two perturbation curves, and Sparsity is the ratio $S_{\max}/S_{\mathrm{mean}}$ of the normalized map. The framework's argument runs by assembling these scores into a single comparison table and reading method selection off the resulting trade-offs.

What would settle it

Recompute the same benchmark with multiple random seeds and per-image distributions: if the gaps reported in the paper, such as D-CLOSE's OA of 0.881 versus G-CAME's 0.644 on MS-COCO/YOLOX or G-CAME's PG of 94.31% versus D-CLOSE's 87.86%, overlap under resampling, the claimed trade-offs would not be established.

Watch

Extended reading notes

Core claim

On the paper's own terms, ODExAI reliably measures explanation quality and informs method selection for specific detection tasks. Concretely, the benchmark shows a structured trade-off: D-CLOSE achieves the highest faithfulness, with Over-All (OA) values such as 0.881 on MS-COCO with YOLOX and 0.863 with Faster R-CNN, at the cost of runtimes around 70 seconds; G-CAME achieves the best localization, with Pointing Game scores up to 96.13% and Energy-based Pointing Game scores up to 70.11%, and the lowest runtime (0.54s), but lower faithfulness (OA 0.549 in the same Faster R-CNN setting) and more diffuse saliency maps; D-RISE stays moderate across all dimensions without leading on the main higher-is-better metrics. The paper interprets these results as evidence that region-based methods are better suited to auditing and debugging, while CAM-based methods fit real-time, localization-focused deployments.

Load-bearing premise

The whole comparison rests on the assumption that the five selected metrics, each computed once per setting, accurately capture whether an explanation is localized, faithful, and simple, and that the bounding box is the correct spatial target for explanation quality.

Editorial extensions

If this is right

  • If the central claim is right, a real-time object detection pipeline should prefer a CAM-based explainer like G-CAME when localization is the priority, accepting lower faithfulness.
  • For auditing or debugging, where the explanation must track the model's actual reasoning, a region-based method like D-CLOSE is the defensible choice despite its high runtime.
  • Reporting a single evaluation metric, such as faithfulness alone, would mislead: the three dimensions must be reported together to see the trade-off profile.
  • ODExAI provides a repeatable protocol: any new explainer can be inserted and scored on the same five metrics across the same detectors and datasets, making comparisons standardizable.
  • A method that is moderate on every axis, such as D-RISE, remains useful as a fallback when the target dimension is unspecified or when explanation diversity is valued.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: re-running the same evaluation with segmentation masks or human-annotated regions of interest instead of bounding boxes could shift the localization rankings, especially G-CAME's lead, and this is directly testable with the paper's released benchmark.
  • Editorial inference: because each cell in the benchmark is a single number, resampling across images and random seeds and reporting confidence intervals would show which of the advertised gaps are stable; I would expect some gaps to shrink substantially.
  • Editorial inference: applying ODExAI to transformer-based detectors, where attention maps play a different role than convolutional feature maps, would test whether the low faithfulness of CAM-based methods is a general property or an artifact of the two detectors benchmarked here.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces ODExAI, a framework for evaluating explainable AI (XAI) methods for object detection along three dimensions: localization accuracy (PG, EBPG), faithfulness to the model (Deletion/Insertion, OA), and computational complexity (Sparsity, runtime). The framework is demonstrated in a 'toy experiment' comparing D-CLOSE, G-CAME, and D-RISE on YOLOX and Faster R-CNN using MS-COCO and PASCAL VOC, with results reported in Table 1 and visualized as spider plots in Figure 5. The central empirical claim, stated in Section 4.3, is that ODExAI 'reliably measures explanation quality and informs method selection,' leading to conclusions that D-CLOSE is most faithful but slow, G-CAME is best localized and fastest but less faithful, and D-RISE is moderate across all dimensions.

Significance. If the central claim were fully supported, ODExAI would be a useful standardized benchmark for comparing XAI methods in object detection, a domain where no such consensus framework exists. The paper's strengths are the use of standard, correctly cited metrics (PG, EBPG, Deletion/Insertion, OA, Sparsity) and the public release of code at the provided GitHub URL. However, as it stands, the paper delivers a demonstration rather than a validated evaluation standard: the empirical evidence consists of single point estimates from a self-described toy experiment, with no error bars, sample sizes, statistical tests, or seed variance reported, and Section 5.2 concedes that the framework lacks a proper normalization scheme, which can be misleading when metrics differ in scale or unit. These issues directly affect the load-bearing claim that ODExAI reliably measures explanation quality, so the contribution is currently a framework proposal with illustrative results rather than a fully supported empirical benchmark.

major comments (3)
  1. [Section 4.3, Table 1] The central claim that ODExAI 'reliably measures explanation quality and informs method selection' is not supported by the reported evidence. Each cell of Table 1 is a single point estimate with no variance, sample size, or statistical test; D-CLOSE and D-RISE are stochastic methods using 2000 random perturbation masks, so seed variance should be reported to establish that the observed differences are not noise. The paper itself labels the study a 'toy experiment' in the contribution list, which further weakens the basis for the reliability claim.
  2. [Section 5.2, Figure 5] The framework's own stated limitation undermines the trade-off analysis. Section 5.2 admits that the framework 'lacks a proper normalization scheme, which can be misleading when metrics differ in scale or unit' and that metrics mix higher-better and lower-better directions. Since Table 1 and Figure 5 plot raw OA (an AUC difference), PG/EBPG percentages, Sparsity (a ratio with unbounded upper range), and runtime in seconds on the same spider plot, the visual shape and the resulting trade-off narrative (e.g., D-CLOSE faithful but slow, G-CAME localized but less faithful) may be artifacts of scale rather than of method quality. The skill-score baseline cited as reference [5] is mentioned only as future work, so no random or reference baseline is used to put the raw values in context.
  3. [Section 3.2] The localization metrics PG and EBPG assume that the ground-truth bounding box is the correct region of interest for an explanation, but this assumption is not validated. Object-detection explanations may reasonably include contextual or surrounding pixels that contribute to the model's decision, so restricting localization to the bounding box could systematically disadvantage methods that capture context. Since localization is one of the three headline dimensions, the choice should be justified empirically, for example by a sensitivity analysis using different ROI definitions, or explicitly discussed as a limitation with a concrete test.
minor comments (5)
  1. [Table 1] The PG row contains concatenated values without delimiters, such as '94.3186.55' and '96.1384.12', making the table difficult to read; the values should be separated clearly.
  2. [Section 2.1] DETR is cited as reference [57], but [57] is the FCOS paper; DETR corresponds to the Carion et al. reference currently numbered [9], and Deformable DETR should have its own distinct citation.
  3. [References] References [4] and [5] are identical (both Bommer et al., 'Finding the right XAI method'), which creates a duplicate entry; the duplicate should be merged and the in-text citation for the skill score corrected accordingly.
  4. [Section 5.2] The proposed solution of 'assign weights' to handle higher-better versus lower-better metrics is only a sentence; the paper should specify how the weights would be determined so that they do not introduce a new arbitrary degree of freedom.
  5. [Figure 5] The spider plot axes are not labeled with units or any normalization information; without such labels, the visualization cannot be interpreted across metrics with different physical dimensions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ODExAI is an empirical benchmark built on externally published metrics and independent XAI implementations, with no equation reducing to its own inputs.

full rationale

The paper's central claim is that ODExAI reliably measures explanation quality, supported by a toy experiment comparing D-CLOSE, G-CAME, and D-RISE. The metrics (PG, EBPG, Deletion/Insertion, OA, Sparsity, runtime) are standard externally published measures, and the XAI methods are cited external implementations, not derived from the framework. No parameter is fitted to a subset of data and then renamed a prediction; no uniqueness theorem from the authors is invoked; and no equation such as Eq. (1), (2), or (3) reduces to the conclusions about method ranking. The Section 5.2 caveat about missing normalization and mixed higher/lower-better metrics is a validity concern about comparing raw scales, not a circularity issue. Accordingly, the score is 0, and no circular steps are listed.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests on standard metrics borrowed from classification/explanation literature plus the specific experimental setup. The main unverified premises are the validity of these metrics for detection, the representativeness of the toy experiment, and the correctness of the released code.

free parameters (2)
  • Number of perturbed masks (D-CLOSE and D-RISE) = 2000
    Chosen experimental hyperparameter; not fitted but affects all region-based results.
  • Test image subset size = not reported
    The paper does not specify how many images from MS-COCO and PASCAL VOC were evaluated, making the exact experimental scale and reproducibility unclear.
assumptions (4)
  • domain assumption The bounding box of the target object is the correct region of interest for evaluating localization
    Used in PG and EBPG metrics; explanation methods that highlight context beyond the box may be undervalued (Section 3.2).
  • domain assumption The chosen metrics (PG, EBPG, Del/Ins, OA, Sparsity) are valid and sufficient for measuring the three intended dimensions
    The framework relies on established metrics from classification literature, but their validity for detection explanations is assumed (Section 3).
  • domain assumption YOLOX and Faster R-CNN are representative detectors and MS-COCO and PASCAL VOC are representative datasets
    The toy experiment uses only these two architectures and two datasets, and results are presented as general trade-offs (Section 4.1).
  • ad hoc to paper The implementation correctly computes the saliency maps and metrics
    The paper does not provide verification of the code or cross-checks against reference implementations; correctness is assumed (Section 4.3).

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Cite this review

Pith. "Pith review of ODExAI: A Comprehensive Object Detection Explainable AI Evaluation." pith.science (2026). https://pith.science/paper/7XNDWT2X

@misc{pith2026250419249,
  author       = {Pith},
  title        = {Pith review of: ODExAI: A Comprehensive Object Detection Explainable AI Evaluation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7XNDWT2X}},
  note         = {Machine review of arXiv:2504.19249}
}
read the original abstract

Explainable Artificial Intelligence (XAI) techniques for interpreting object detection models remain in an early stage, with no established standards for systematic evaluation. This absence of consensus hinders both the comparative analysis of methods and the informed selection of suitable approaches. To address this gap, we introduce the Object Detection Explainable AI Evaluation (ODExAI), a comprehensive framework designed to assess XAI methods in object detection based on three core dimensions: localization accuracy, faithfulness to model behavior, and computational complexity. We benchmark a set of XAI methods across two widely used object detectors (YOLOX and Faster R-CNN) and standard datasets (MS-COCO and PASCAL VOC). Empirical results demonstrate that region-based methods (e.g., D-CLOSE) achieve strong localization (PG = 88.49%) and high model faithfulness (OA = 0.863), though with substantial computational overhead (Time = 71.42s). On the other hand, CAM-based methods (e.g., G-CAME) achieve superior localization (PG = 96.13%) and significantly lower runtime (Time = 0.54s), but at the expense of reduced faithfulness (OA = 0.549). These findings demonstrate critical trade-offs among existing XAI approaches and reinforce the need for task-specific evaluation when deploying them in object detection pipelines. Our implementation and evaluation benchmarks are publicly available at: https://github.com/Analytics-Everywhere-Lab/odexai.

Figures

Figures reproduced from arXiv: 2504.19249 by the authors.

Figure 1
Figure 1. Overview of the architecture and web interface of our proposed Object [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Demonstration of the localization property using region-specific relevance [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Illustration of the faithfulness property using progressive perturbation. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Visualization of the complexity property via relevance score distributions. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: A visualization of the ODExAI framework for quantitative metrics of [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.