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REVIEW 4 major objections 7 minor 41 references

Achieving 3D Attention via Triplet Squeeze and Excitation Block

T0 review · 4 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A new attention block combining Triplet Attention and Squeeze-and-Excitation reports 78.27% on FER2013.

desk verdict An honest engineering paper whose controlled comparison supports the TripSE block, but the headline 78.27% FER2013 SOTA is a single selected run and needs seeds and a clear test/validation statement before it can be taken at face value. read the letter →

arxiv 2505.05943 v1 pith:WFG6KYX2 submitted 2025-05-09 cs.CV cs.AIcs.LG

Maan Alhazmi , Abdulrahman Altahhan This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords attentionmechanismTripletSqueeze-and-Excitation3DfacialexpressionrecognitionFER2013ConvNeXttransferlearning
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

The paper claims that fusing two existing attention ideas, Triplet Attention (TA) and Squeeze-and-Excitation (SE), into a single block produces a form of 3D attention that improves CNN image classifiers, especially for facial expression recognition. The proposed TripSE block is inserted after each group of blocks in ResNet18, DenseNet121, and ConvNeXt, adding only about 0.1 million parameters. On the FER2013 benchmark the authors report that ConvNeXt-S with the TripSE4 variant and reduction ratio $r=1$ reaches 78.27% accuracy, which they call a new state of the art for this dataset. The same block also yields gains on AffectNet, CIFAR100, and ImageNet, and the paper argues this shows CNNs with cheap attention remain competitive in vision tasks.

What carries the argument

The central object is the TripSE attention block. It takes TA's three rotational branches, operating over the $C \times W$, $C \times H$, and $W \times H$ faces of the tensor, each producing a 2D attention map through z-pooling, convolution, batch normalisation, and sigmoid, and fuses each map with the output of an SE module that computes global channel weights from the permuted tensor. Depending on the variant, the SE sits before, after, or parallel to the TA branch; TripSE3 and TripSE4 run SE in parallel with each branch, and TripSE4 adds a shift before the sigmoid and a final SE at the end, yielding a full 3D weighting tensor used to scale the branch input. The block's work is to let inter-dimensional attention and global channel importance modulate each other before the three branches are combined.

What would settle it

Re-run ConvNeXt-S plus TripSE4 ($r=1$) on FER2013 under the standard protocol: fix the configuration before evaluation, report the split, run multiple seeds, and compare the mean and variance against the 76.82% baseline. If the mean is below 78.27% or within noise of the plain ConvNeXt-S score of 77.19%, the state-of-the-art claim is not sustained.

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Extended reading notes

Core claim

On its own terms, the paper discovers that the gap between TA's cross-dimension attention and SE's global channel weighting can be closed by a multiplicative, and in TripSE4 affine, fusion of the two, producing a 3D attention tensor. In a controlled comparison on FER2013, ConvNeXt-T with TripSE1 reaches 77.86%, above the reported reproducible baseline of 76.82% from a residual masking network ensemble, and ConvNeXt-S with TripSE4 ($r=1$) reaches 78.27%. The authors attribute the success of TripSE4 to its parallel SE branches with a shift-and-scale transformation per rotational channel and a final SE at branch unification, which they say preserves information better than simple averaging. They also report that adding SE alone or TA alone to ConvNeXt lowers FER2013 accuracy relative to the plain backbone, whereas TripSE raises it.

Load-bearing premise

The headline 78.27% result rests on the assumption that a single run, whose split is not stated and whose configuration was chosen after seeing all variant results, is a trustworthy estimate of generalization on FER2013.

Editorial extensions

If this is right

  • If the FER2013 result holds, a simple plug-in attention block can lift existing CNN backbones above published facial-expression state of the art without an architectural redesign.
  • The controlled comparisons imply that TA alone and SE alone are not enough on this task; their fusion is what produces the reported gain.
  • The near-zero parameter overhead (28.6 to 28.7 million on ConvNeXt-T) means the block is cheap enough to add to deployed models.
  • The AffectNet improvement, from 62.87% to 64.16%, suggests the benefit is not specific to a single dataset.
  • The underperformance of TripSE2 and TripSE3 relative to TripSE1 and TripSE4 is presented as evidence that 3D scaling and a final SE at unification are load-bearing design choices.

Reading between the lines

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

  • The reported 78.27% comes from a configuration selected after seeing multiple variant and ratio results, so independent multi-seed reproduction with a pre-registered configuration is needed to know how much of the gap over baselines is real.
  • The same fusion idea may transfer to other small-sample vision tasks, such as medical image classification, where global channel statistics are informative and overfitting risk is high.
  • A natural testable extension is to vary the reduction ratio and branch placement systematically across more backbones, and to measure whether TripSE's benefit grows with the number of inserted blocks.
  • Because the paper does not state whether the FER2013 number is measured on the test or validation split, any comparison against published leaderboard results is provisional until the protocol is clarified.
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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

4 major / 7 minor

Summary. The paper proposes a family of attention blocks, TripSE1-4, that combine Triplet Attention (TA) and Squeeze-and-Excitation (SE) to produce what the authors call 3D attention maps. The blocks are inserted after each group of blocks in ResNet18, DenseNet, and ConvNeXt, and the resulting models are evaluated on CIFAR100, ImageNet, FER2013, and AffectNet. The central claim is that ConvNeXt-S with TripSE4 (r=1) achieves 78.27% accuracy on FER2013, described as a new state-of-the-art and 'an unprecedented feat' (Section V.C, Table III). The paper also reports a controlled comparison on FER2013 in which TripSE1 outperforms both TA-only and SE-only variants under the same finetuning protocol.

Significance. If the central claim holds, the work offers a simple, parameter-efficient way to improve CNN-based facial expression recognition: the TripSE block adds only about 0.1M parameters to ConvNeXt-T (28.6M vs 28.7M) while reportedly gaining over one point on FER2013 relative to the ConvNeXt-S baseline and 1.45 points over the stated reproducible baseline of 76.82% (Section V.C, Table III). The controlled comparison on FER2013 is a genuine strength: under the same finetuning protocol, TripSE1 reaches 78.08% against 76.51% for TA and 76.93% for SE, which supports the claim that the proposed combination, rather than a generic attention module, is responsible for the gain. The significance is conditional, however, on the 78.27% headline number being a reproducible estimate of generalization; the paper provides no seeds, no error bars, and no explicit statement of whether the result is measured on the official test split. The paper does not include code or any machine-checked derivations, so the contribution rests entirely on the empirical results.

major comments (4)
  1. [Section V.C, Table III] The state-of-the-art claim of 78.27% rests on a single run with no seeds, no standard deviations, and no explicit statement of whether FER2013 accuracy is measured on the official test split (3,589 images) or the validation split. Because the headline number is the maximum over four variants and two reduction ratios (eight configurations reported in Table III), the result may reflect selection on the test set rather than a genuine improvement. Please report mean and standard deviation over at least three seeds, state clearly which split is used, and either preselect the variant and r before evaluation or apply an appropriate multiple-comparison correction.
  2. [Table III and Section V.C] The internal evidence for the robustness of the proposal is mixed. ConvNeXt-S + TripSE1 (r=1) scores 76.61%, below the 77.19% baseline, and ConvNeXt-S + TripSE4 (r=16) scores 76.71%, also below baseline, while the r=16/r=1 counterparts achieve 78.08% and 78.27%. The text states that TripSE1 'consistently demonstrated the effectiveness' of the block and that the original ConvNeXt-S failed to gain when scaled up, but the table shows the improvement disappears or reverses for some configurations. This instability requires explanation; at minimum, all runs, the selection procedure, and the sensitivity to r should be reported and discussed honestly.
  3. [Section III.C and III.D] The method is specified only verbally and through figures. Terms such as 'rotational channels', 'rotational maps', 'multiplicative or translational broadcasting', and the exact position of the SE block in each variant are not defined with equations or tensor-shape diagrams. This makes the four variants impossible to reproduce or formally check. Please provide precise mathematical definitions of TripSE1-4, including where the SE block is applied, how the 1D SE vector is expanded and combined with the 2D TA attention map, and what operation occurs at branch unification.
  4. [Section IV.B] The implementation details omit key protocol elements needed to interpret the FER2013 numbers: the evaluation split (validation vs test), any normalization used for the resized 224x224 images, and whether the learning-rate scheduler is driven by validation accuracy and then followed by evaluation on the test set. Given that the FER2013 test labels are public and that validation-set evaluation is common in this literature, the paper must state explicitly which split produced the reported numbers and how early stopping was applied.
minor comments (7)
  1. [Abstract] The abstract says 'a new feet for this dataset' and should read 'a new feat'.
  2. [Table II footnote] The footnote contains a typo: 'Trained from scrtach' should be 'Trained from scratch'.
  3. [Section V.C] The text reports the ConvNeXt-T baseline as 77.20%, while Table III reports 77.19% for the same model; the inconsistency should be corrected.
  4. [Table III] The rows for ConvNeXt-T + SE and ConvNeXt-T + TA are labeled inconsistently; the SE row has no reduction ratio, and the TA row is listed as ConvNeXt-T + TA1 but discussed as the TA block. Please add the exact hyperparameters used for these baselines.
  5. [III.D] Figure 2's caption contains 'visa-versa', which should be 'vice versa'.
  6. [III.C] The sentence 'we consolidate the resultant 3 3D rotational attention maps' appears to contain a duplicated '3'; it should read 'three 3D'.
  7. [References] Reference [33] is malformed: the author list contains a garbled fragment ('and D. H., "Lee,"Y. Zhou, ...') that should be cleaned up.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: TripSE is an empirical architecture combination and the FER2013 SOTA claim is a reported measurement, not a quantity derived from its own inputs.

full rationale

The paper does not contain a derivation chain that could reduce to its own inputs. TripSE is defined in Section III.C as an explicit combination of the external Triplet Attention module [12] and Squeeze-and-Excitation [9]; the four variants in Section III.D are architectural compositions described by forward-pass operations, and the reported accuracies in Table III are empirical outcomes of finetuning on FER2013. No equation in the paper defines TripSE's expected accuracy as a function of baseline accuracy, and no fitted parameter is renamed as a prediction. The winning configuration (TripSE4, r=1) is selected after observing evaluation results across several variants, which is a model-selection and statistical-robustness concern (single run, unspecified test/validation split), not a circularity reduction. The only self-citation is [30] (Alhazmi and Altahhan), used in Section II.B to report that Triplet Attention performed best among CBAM/BAM/TA in prior work; this background claim is not load-bearing for the central empirical result, which is compared against many external methods in Table III. There is no uniqueness theorem, no imported ansatz, and no renaming of a known result: the block outputs are new combinations, and the SOTA claim is externally falsifiable by reproducing the stated training procedure. Therefore no circular step is exhibited.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper contributes no new physical or mathematical entities; it is an architectural combination of existing attention blocks. The free parameters are the SE reduction ratio and the selection among four stated variants, both chosen on test-set performance. The axiomatic load is carried by the general attention hypothesis and by the paper's specific 'rotational channel' heuristic, neither of which is proven.

free parameters (2)
  • SE reduction ratio r = 1 (best FER2013), also 16
    Two values tested; r=1 reported for the headline 78.27%, r=16 reported for TripSE1 78.08%. Choice is based on test-set performance.
  • TripSE variant selection = TripSE4 for headline; TripSE1 for most claims
    Four architectural variants were evaluated, and the best-performing one on FER2013 was selected for the state-of-the-art claim. This is a categorical free choice fitted to the benchmark.
assumptions (3)
  • domain assumption Applying learned attention maps via element-wise multiplication improves feature discrimination in CNNs.
    The entire method relies on this general deep learning practice; the paper does not prove it.
  • ad hoc to paper Rotating feature tensors along W and H channels creates meaningful 'rotational channels' that can be Squeeze-and-Excitation weighted.
    Section III-C introduces 'rotational channels' as the key premise for extending SE into the spatial dimensions; no theoretical justification is provided beyond the heuristic claim.
  • domain assumption Pretrained ImageNet weights remain effective when four new attention blocks are inserted after each group of blocks.
    Section III-E asserts 'this will make the structural enhancements ... more compatible', but no evidence is given that transfer learning is preserved; the experiments implicitly assume it.

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Pith. "Pith review of Achieving 3D Attention via Triplet Squeeze and Excitation Block." pith.science (2026). https://pith.science/paper/WFG6KYX2

@misc{pith2026250505943,
  author       = {Pith},
  title        = {Pith review of: Achieving 3D Attention via Triplet Squeeze and Excitation Block},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WFG6KYX2}},
  note         = {Machine review of arXiv:2505.05943}
}
read the original abstract

The emergence of ConvNeXt and its variants has reaffirmed the conceptual and structural suitability of CNN-based models for vision tasks, re-establishing them as key players in image classification in general, and in facial expression recognition (FER) in particular. In this paper, we propose a new set of models that build on these advancements by incorporating a new set of attention mechanisms that combines Triplet attention with Squeeze-and-Excitation (TripSE) in four different variants. We demonstrate the effectiveness of these variants by applying them to the ResNet18, DenseNet and ConvNext architectures to validate their versatility and impact. Our study shows that incorporating a TripSE block in these CNN models boosts their performances, particularly for the ConvNeXt architecture, indicating its utility. We evaluate the proposed mechanisms and associated models across four datasets, namely CIFAR100, ImageNet, FER2013 and AffectNet datasets, where ConvNext with TripSE achieves state-of-the-art results with an accuracy of \textbf{78.27\%} on the popular FER2013 dataset, a new feat for this dataset.

Figures

Figures reproduced from arXiv: 2505.05943 by the authors.

Figure 1
Figure 1. ConvNext, DenseNet, and ResNet architectures incorporated with the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Triplet Attention with Squeeze-and-Excitation Blocks 1 (TripSE1) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Triplet Attention with Squeeze-and-Excitation Block 3 (TripSE 3). [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Triplet Attention with Squeeze-and-Excitation Block 4 (TripSE 4). [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

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