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arxiv: 2605.08325 · v1 · submitted 2026-05-08 · 📡 eess.IV · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

CAMAL: Improving Attention Alignment and Faithfulness with Segmentation Masks

Jin Song Dong, Manuel Rigger, Rajdeep Singh Hundal, Yan Xiao

Pith reviewed 2026-05-12 00:54 UTC · model grok-4.3

classification 📡 eess.IV cs.AIcs.LG
keywords attention alignmentattention faithfulnesssegmentation masksauxiliary regularizationclass activation mapsexplainabilitydeep learningreinforcement learning
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The pith

CAMAL uses segmentation masks as an auxiliary regularizer to lift attention faithfulness by over 35 percent in vision models

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

The paper introduces CAMAL to turn existing segmentation masks into a training signal that steers model attention. It extracts the model's attention map on each image, measures overlap with the mask's marked discriminative regions, and adds a loss term that rewards alignment inside those regions while penalizing attention outside them. The same procedure is applied during both ordinary supervised training and deep reinforcement learning. The authors report that attention becomes statistically better aligned with the masks and that the attention exerts measurably more causal influence on the final decision, producing more than 35 percent higher faithfulness scores than recent baselines. These changes improve explainability while leaving generalization unchanged or slightly better and adding zero cost at inference time.

Core claim

CAMAL extracts the model's attention for each image during training and compares the attention to ground-truth discriminative regions obtained from the corresponding segmentation masks. CAMAL then acts as an auxiliary regularizer, encouraging attention that aligns with ground-truth discriminative regions, while suppressing attention elsewhere. This produces statistically significant gains in attention alignment across all settings and improves attention faithfulness by over 35 percent compared to recent work, while enhanced explainability and improved or comparable generalization occur without increasing inference cost.

What carries the argument

CAMAL, an auxiliary regularization loss applied during training that penalizes mismatch between extracted attention maps and the regions labeled by segmentation masks

If this is right

  • Attention alignment improves with statistical significance in every tested setting.
  • Attention faithfulness rises by more than 35 percent relative to recent baselines.
  • Model decisions become more explainable because attention better reflects causal input factors.
  • Generalization performance stays the same or improves.
  • No extra computation is required at inference time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Any vision task that already collects segmentation masks can adopt the regularizer with no new labeling.
  • The mask-driven loss could be inserted into other attention-based models not examined in the paper.
  • If the supplied masks contain systematic errors, the regularizer may reinforce those errors rather than correct them.

Load-bearing premise

Ground-truth segmentation masks correctly mark the precise regions that should drive the model's decisions.

What would settle it

An occlusion test in which removing the mask-marked regions from test images produces no larger drop in prediction for CAMAL-trained models than for baseline models.

Figures

Figures reproduced from arXiv: 2605.08325 by Jin Song Dong, Manuel Rigger, Rajdeep Singh Hundal, Yan Xiao.

Figure 1
Figure 1. Figure 1: Architecture diagram for CAMAL. Given an input image I c passed through a model (steps 1–2), CAMAL extracts the model’s attention Hc (steps 3–4) and compares it with ground-truth discriminative regions obtained from the corresponding segmentation mask Mc (step 5). Lastly, CAMAL regularizes the network by encouraging attention within ground-truth discriminative regions (α ↑) while suppressing attention in s… view at source ↗
Figure 2
Figure 2. Figure 2: Training overhead incurred by per-sample attention extraction rel￾ative to batch-level attention extraction. Values are based on empirical measure￾ments from our experiments where avail￾able, with remaining points estimated from known scaling trends. Existing attention supervision approaches do not scale well to large datasets, as the gradient-based attention ex￾traction techniques they rely on are typical… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of attention alignment between [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of attention alignment between [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of attention faithfulness between [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of generalization between CAMAL and Vanilla in DRL. The left figure uses SBCI to statistically analyze the results across environments and trials while the right figure uses POI to determine if the observed differences are statistically significant (P(Left Return > Right Return) > 0.5 ∧ 0.5 ∈/ CI). The shaded regions denote 95% confidence bands [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The SBCI evaluation method involves sampling a DRL algorithm’s result set repeatedly [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison between pseudo-discriminative regions derived from an external prior (CLIP) [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of regularizers under different perturbations—shift, erode, and dilate. For [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of attention alignment between [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of attention alignment between [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of attention faithfulness between [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
read the original abstract

Many vision datasets now provide segmentation masks in addition to annotated images to support a wide range of tasks. In this work, we propose Class Activation Map Attention Learning (CAMAL), an efficient and scalable method that utilizes segmentation masks to improve attention alignment and faithfulness in vision models. Specifically, attention alignment refers to the degree to which a model's attention aligns with ground-truth discriminative regions, while attention faithfulness refers to the degree to which a model's attention influences its decision. Improving both attention alignment and faithfulness is essential for ensuring that model attention is both spatially accurate and causally meaningful. To improve attention alignment and faithfulness in vision models, CAMAL first extracts the model's attention for each image during training and then compares the attention to ground-truth discriminative regions obtained from the corresponding segmentation masks. CAMAL then acts as an auxiliary regularizer, encouraging attention that aligns with ground-truth discriminative regions, while suppressing attention elsewhere. We evaluated CAMAL across two learning paradigms -- Deep Learning (DL) and Deep Reinforcement Learning (DRL) -- and observed consistent, significant improvements in both attention alignment and faithfulness. In particular, CAMAL yields statistically significant gains in attention alignment across all settings, and improves attention faithfulness by over 35% compared to recent work. Moreover, we show that improved attention alignment and faithfulness enhance explainability, while yielding improved or comparable generalization performance without increasing inference cost. These findings demonstrate that the spatial information contained within segmentation masks can be effectively leveraged to guide model attention across learning tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes CAMAL, an auxiliary regularization method that extracts model attention during training and aligns it to ground-truth discriminative regions derived from segmentation masks, while suppressing attention outside those regions. It evaluates the approach in both supervised deep learning and deep reinforcement learning settings, claiming statistically significant gains in attention alignment across all tested configurations and over 35% improvement in attention faithfulness relative to recent baselines, together with enhanced explainability and comparable or better generalization at no added inference cost.

Significance. If the empirical claims hold under rigorous controls, the work offers a practical, low-overhead way to exploit existing segmentation annotations for improving the spatial accuracy and causal relevance of attention maps, which could strengthen the reliability of post-hoc explanations in vision models without altering inference-time behavior.

major comments (2)
  1. [Abstract and §4] Abstract and §4 (Experiments): the central claim of >35% faithfulness improvement and statistically significant alignment gains is asserted without reporting the precise faithfulness metric (e.g., insertion/deletion AUC, perturbation-based scores), the exact baseline implementations, the statistical tests used, sample sizes, or any ablation that isolates the mask-specific regularizer from generic attention regularization effects. This information is load-bearing for evaluating whether the reported gains are causal or artifactual.
  2. [§3] §3 (Method): the auxiliary loss assumes segmentation masks identify the regions that actually drive the model's decisions. If the model relies on cues outside the masks (background context, texture, or co-occurring features), the regularizer could alter attention statistics without changing the underlying decision process. The manuscript should include targeted ablations or distribution-shift tests that demonstrate the faithfulness gains are mask-dependent rather than generic.
minor comments (2)
  1. [§2] §2 (Related Work): add explicit comparison to other mask-guided attention methods (e.g., those using saliency or CAM supervision) to clarify the incremental contribution.
  2. [Figure 1 and §3.2] Figure 1 and §3.2: the diagram and loss formulation would benefit from an explicit equation showing how the alignment term is weighted against the primary task loss and how attention is extracted (e.g., from which layer or head).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below. Where the concerns identify gaps in clarity or supporting evidence, we have revised the manuscript to incorporate the requested details and additional experiments.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Experiments): the central claim of >35% faithfulness improvement and statistically significant alignment gains is asserted without reporting the precise faithfulness metric (e.g., insertion/deletion AUC, perturbation-based scores), the exact baseline implementations, the statistical tests used, sample sizes, or any ablation that isolates the mask-specific regularizer from generic attention regularization effects. This information is load-bearing for evaluating whether the reported gains are causal or artifactual.

    Authors: We agree that these specifics are necessary to substantiate the claims. The faithfulness metric is the insertion/deletion AUC (as introduced in the cited perturbation literature), and the >35% figure is the relative improvement on this metric. In the revised manuscript we have: (1) updated the abstract to name the metric explicitly; (2) expanded the opening of §4 with a dedicated evaluation-protocol subsection that defines the AUC computation, lists all baseline implementations with exact hyperparameters and code references, reports the statistical tests (paired t-tests across 5 independent runs, with p-values and sample sizes n=1000 images per dataset), and (3) added a new ablation that replaces the mask-guided term with a generic attention regularizer (entropy minimization). The ablation shows that mask-specific alignment produces statistically larger gains than the generic baseline, supporting that the reported improvements are not artifactual. revision: yes

  2. Referee: [§3] §3 (Method): the auxiliary loss assumes segmentation masks identify the regions that actually drive the model's decisions. If the model relies on cues outside the masks (background context, texture, or co-occurring features), the regularizer could alter attention statistics without changing the underlying decision process. The manuscript should include targeted ablations or distribution-shift tests that demonstrate the faithfulness gains are mask-dependent rather than generic.

    Authors: This is a substantive point about the causal link between mask alignment and decision change. While the consistent faithfulness gains across DL and DRL settings already suggest that the masks overlap with decision-relevant regions in the evaluated datasets, we accept that explicit controls are required. The revised §4 now contains two targeted experiments: (i) an ablation substituting ground-truth masks with random or uniform masks, which eliminates the faithfulness improvement and often degrades performance; (ii) a controlled distribution-shift test on a modified dataset variant in which background textures are altered while foreground masks remain unchanged. Under this shift, CAMAL retains its gains only when the masks continue to mark causally relevant areas, confirming that the regularizer’s benefit is mask-dependent rather than a generic attention effect. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper proposes CAMAL as an auxiliary regularizer that takes external ground-truth segmentation masks as an independent source of supervision to encourage attention alignment during training. The claimed gains in alignment and faithfulness are measured post-training against separate metrics and baselines, without any equations or steps that reduce by construction to the model's own outputs, fitted parameters renamed as predictions, or load-bearing self-citations. The central claims remain empirically testable against external masks and standard faithfulness protocols rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract introduces no explicit free parameters, mathematical axioms, or new postulated entities; the method is described purely in terms of standard attention extraction and regularization against provided masks.

pith-pipeline@v0.9.0 · 5572 in / 1214 out tokens · 47413 ms · 2026-05-12T00:54:15.930452+00:00 · methodology

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Lean theorems connected to this paper

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Reference graph

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