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REVIEW 3 major objections 5 minor 37 references

Suppressing Uncertainty in Gaze Estimation

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read By comparing three labels per image—ground truth, prediction, and neighbor-derived label—SUGE detects unreliable samples, then weights and corrects them, improving gaze estimation.

desk verdict A novel triplet-label consistency method for gaze estimation with small but consistent gains; the neighboring-label construction needs a nonnegativity fix and more rigorous evaluation. read the letter →

arxiv 2412.12890 v1 pith:XWRP4HVS submitted 2024-12-17 cs.CV cs.LG

Shijing Wang , Yaping Huang This is my paper · ORCID
classification cs.CVcs.LG
keywords gazeestimationuncertaintylabelnoisetriplet-labelconsistencyneighboringlabelssampleweightingcorrectionco-training
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

Gaze datasets collected in real settings contain two kinds of bad training material: images that are occluded, blurred, or even show no face at all, and labels that do not line up with where the person actually looked. Training on such examples pulls gaze models away from accurate predictions. The paper proposes SUGE, which compares three labels for each training image—the ground-truth annotation, the model's own predicted pseudo label, and a neighboring label assembled from similar images' labels—and turns the pairwise angular disagreements into confidence scores for both the image and the label. Low image confidence down-weights the sample in the loss; low label confidence triggers a label correction that blends the original annotation with pseudo and neighboring labels. This is worth caring about because annotation and image noise are unavoidable in gaze datasets, so a training procedure that detects and suppresses them can improve gaze estimation without any extra supervision.

What carries the argument

The mechanism that carries the argument is the neighboring label, defined as the reconstruction-weighted average of ground-truth labels of the K nearest neighbors of an image in the encoder's feature space, restricted to the same person. It gives each sample a third label that is independent of both the possibly wrong annotation and the model's own prediction. Around this, the paper defines two uncertainty statistics: TupleMD, $\min(D_{pg}, D_{ng})/(D_{pn} + \epsilon)$, which scores label quality by how far the ground-truth label stands from both other labels, and TripleMD, $\min(D_{pg}, D_{pn}, D_{ng})$, which scores image quality by the largest disagreement among any pair of the three labels. A two-component GMM converts these statistics into label and image confidences; sample weighting puts the image confidence into the loss $L = \sum_i \hat{W}_i \|\hat{Y}_i - f(E(X_i))\|_1$, and label correction blends ground-truth, pseudo, neighboring, and flipped labels according to label confidence. A co-training loop lets two networks exchange their corrected labels and weights to avoid overconfidence in self-generated confidences.

What would settle it

Re-annotate a random subset of EyeDiap or Gaze360 training images independently and check whether samples flagged with low label confidence are precisely those whose original labels disagree with the re-annotation, and whether the corrected labels land closer to the re-annotation; if they do not, the triplet-label uncertainty is not measuring real label noise.

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Extended reading notes

Core claim

The central claim is that a triplet-label consistency measure can detect which gaze training samples are unreliable and then suppress the damage they cause, without needing a clean external set. For each sample, the method builds a neighboring label $\hat{Y}^n_i = \sum_{j=1}^{K} Y_{ij} A_{ij}$ from reconstruction-weighted ground-truth labels of the sample's K nearest same-person neighbors in the encoder's feature space, and places it alongside the ground-truth label $Y_i$ and the pseudo label $\hat{Y}^p_i$. Two angular statistics, TupleMD and TripleMD, are fed to a two-component Gaussian Mixture Model, producing a label confidence and an image confidence. The label confidence drives label correction (a blend of ground truth, pseudo, neighboring, and horizontally flipped labels), the image confidence drives sample weighting, and two networks co-train with exchanged confidences. Concretely, the method lowers mean angular error on EyeDiap from 5.46° to 5.05° with the Gaze360 backbone and from 5.34° to 5.04° with the GazeTR backbone, and on Gaze360 from 10.78° to 10.52° with the Gaze360 backbone.

Load-bearing premise

The method assumes that images that look similar to the model should have similar gaze directions, so a weighted average of nearby images' labels is a trustworthy reference; if this local smoothness fails, the uncertainty scores, the sample weights, and the corrected labels are all biased.

Editorial extensions

If this is right

  • With the Gaze360 backbone, SUGE lowers EyeDiap mean angular error from 5.46° to 5.05° and Gaze360 from 10.78° to 10.52°.
  • The same scheme improves the GazeTR transformer backbone on EyeDiap (5.34° to 5.04°) and Gaze360 (10.61° to 10.51°), showing the uncertainty module transfers across architecture families.
  • SUGE beats two noisy-label learning methods adapted to gaze regression, CoTeaching and DivideMix, on both EyeDiap and Gaze360 with the Gaze360 backbone.
  • Ablations attribute the gain to all designed parts: removing label correction costs 0.12°, removing sample weighting costs 0.05°, removing neighboring labels costs 0.11°, and switching from co-training to self-training costs 0.09°.

Reading between the lines

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

  • The neighboring-label premise could be stress-tested by running SUGE without reducing the feature dimension to 16; if gains shrink when nearest neighbors in high-dimensional space no longer align with label similarity, the local-smoothness assumption is the active ingredient.
  • The same triplet-label recipe should transfer to other continuous regression tasks with locally smooth labels, such as head-pose or body-orientation estimation, where noisy annotations and low-quality frames are also common.
  • The hard confidence truncation at $\tau = 0.5$ discards partial evidence from moderately uncertain samples; a soft, temperature-scaled weighting might extract more signal from them.
  • Because corrected labels are partly built from the model's own pseudo labels, systematic model errors could reinforce annotation errors; co-training reduces but does not remove this risk, so testing with a third independent network is a natural safeguard.
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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 proposes SUGE, a training-time method for gaze estimation that estimates two kinds of uncertainty: image quality and label correctness. For each training sample, it constructs a "neighboring label" by linearly reconstructing the sample's feature vector from K same-person feature neighbors and applying the same weights to their ground-truth labels. The neighboring label, together with the model's pseudo label and the ground-truth label, defines two uncertainty metrics (TupleMD and TripleMD), which are fed to a bimodal GMM to obtain label and image confidences. These confidences are then used to truncate unreliable samples, correct labels via a weighted combination of ground-truth, pseudo, and neighboring labels, and weight the training loss. Two networks are trained simultaneously in a co-training style, exchanging corrected labels and weights. Experiments on EyeDiap, MPIIFaceGaze, and Gaze360 with Gaze360 and GazeTR backbones report consistent improvements over the authors' re-implemented baselines, plus ablations and parameter sensitivity analyses on EyeDiap.

Significance. If the method is sound, the paper addresses a real and under-explored issue in gaze estimation: training-set contamination by low-quality images and imprecise annotations. The proposed triplet-label consistency is a novel mechanism that could generalize to other regression tasks with similarly structured uncertainty. The manuscript is also usefully concrete: it re-implements two strong baselines, provides ablations that isolate each module, and uses held-out benchmark splits for evaluation, so the empirical claim is not circular. However, the central mechanism relies on an unconstrained linear reconstruction whose interpretation as a "weighted average" of neighbor labels is mathematically incorrect, and the reported gains are small and lack statistical support. These issues affect the paper's main claims and require substantial revision.

major comments (3)
  1. [Neighboring Labeling Module, Eqs. (1)-(4)] Eq. (1) minimizes ||E(X_i) - sum_j A_ij E(X_ij)||^2 + lambda ||A_i||^2 subject only to sum_j A_ij = 1, with no nonnegativity constraint on A_ij. The closed form in Eq. (2) can therefore contain negative weights, and Yhat^n_i = sum_j Y_ij A_ij in Eq. (4) is not a weighted average or a convex combination; it can extrapolate outside the range of the neighbors' gaze labels. Since TupleMD (Eq. 8) and TripleMD (Eq. 9) measure angular distances to this Yhat^n, the uncertainty metrics no longer measure local agreement with a smooth label manifold whenever a feature vector lies outside the convex hull of its neighbors, which is a generic situation in a 16-dimensional space with K=4. The ablation 'w/o reconstruction weighting' in Table 3 compares only against a plain average, not against a nonnegative-constrained reconstruction, so it does not test whether the reported gains arise from the claimed local-smoothness prior or from a signed extrapolation artifact. The paper should either impose a nonnegativity constraint (e.g., with a simplex projection) and re-run all experiments, or provide empirical statistics on the frequency and magnitude of negative weights and show that the results are insensitive to them.
  2. [Tables 1-2 and Section 'Comparison with SOTA Gaze Estimation Methods'] The empirical claims are based on single runs without error bars, confidence intervals, or significance tests. The largest reported gain over a re-implemented baseline is 0.41 degrees, but on MPIIFaceGaze SUGE (GazeTR) reports 4.01 vs GazeTR† 4.00, i.e., a slight degradation, and on Gaze360 the GazeTR-based gain is 0.10 degrees. Given the small magnitude of these differences, the phrase 'significantly suggest' in the text is not justified. The authors should report repeated-run statistics (mean and standard deviation over multiple seeds) and, where possible, a paired significance test across the same folds, so that the reader can judge whether the gains are within run-to-run noise.
  3. [Abstract and Table 1] The abstract and conclusion claim 'state-of-the-art performance,' but Table 1 does not support this claim in a straightforward way: SUGE (GazeTR) does not improve over the original GazeTR on MPIIFaceGaze (4.01 vs 4.00), and the comparison set omits several recent gaze estimation methods that report strong results on these benchmarks. The claim should be narrowed to 'improvements over two re-implemented baselines when SUGE is applied,' or the paper should include a comparison against the current state-of-the-art on the same protocols.
minor comments (5)
  1. [Eq. (15)] The loss term is written as ||ˆYi - f(E(Xi)||_1; the norm's closing delimiter is missing and the notation is inconsistent with the rest of the paper. Please use a correctly delimited L1 norm.
  2. [Co-training Strategy and References] The citation '(Friend, Reising, and Cook 1993)' appears to be an education-policy reference on co-teaching, not the machine-learning co-training algorithm; the description in the text (two networks exchanging corrected labels) more closely matches other sources such as Blum and Mitchell (1998) or the co-teaching approach of Han et al. (2018). The authors should replace or supplement the citation appropriately.
  3. [Implementation Details] The statement 'we reduce the feature dimension to 16 at the final layer of the encoder' is not motivated. Since the neighboring label construction is sensitive to the feature space in which neighbors are found, the choice of 16 should be justified or included in the parameter sensitivity analysis.
  4. [Eq. (10) and Algorithm 1] The threshold tau is set to 0.5 for both label and image confidence, but the paper does not discuss whether the optimal tau differs between the two quantities or whether the same threshold is appropriate across datasets. A brief discussion of the truncation operation's effect would improve clarity.
  5. [Visualization Results] Figures 2 and 4 are qualitative; adding quantitative evidence (e.g., the fraction of samples with confidence truncated to zero, or a comparison of corrected versus original labels for a few examples) would strengthen the claim that the uncertainty estimates are meaningful.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SUGE is an empirical self-training pipeline whose gains are measured on held-out benchmarks, and no equation reduces to its own inputs.

full rationale

The paper does not claim a first-principles derivation; it proposes an empirical training procedure. The neighboring label in Eq. 4 is a weighted combination of neighbor ground-truth labels using reconstruction weights from Eq. 2; it is then combined with pseudo and ground-truth labels to define uncertainty metrics in Eqs. 8-9. These metrics are fed to a GMM to produce confidences used for sample weighting and label correction in Eqs. 14-15. No equation is defined in terms of the quantity it is supposed to predict, and no fitted parameter is renamed as a prediction. The self-referential use of the model's own pseudo-labels and confidences is a co-training/self-training loop shared with DivideMix, not a circular derivation. Reported improvements are evaluated on held-out test splits of EyeDiap, MPIIFaceGaze, and Gaze360 against re-implemented baselines, so the core claim is externally falsifiable. The only apparent self-citation (Zhou et al. 2023) appears as a related-work pointer and is not load-bearing. The reviewer concern that Eq. 2's reconstruction weights can be negative, making Eq. 4 a signed extrapolation rather than a true convex weighted average, is a correctness or robustness issue about the neighboring-label construction, not a circularity: it does not make the output equivalent to an input by construction. Therefore the appropriate finding is no significant circularity.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The method's gains rest on an unverified label-smoothness assumption, a bimodal GMM split, and several tuned hyperparameters. No code artifacts or pretrained models are provided.

free parameters (5)
  • K (number of neighbors) = 4
    Chosen by scanning K in {2,4,6} on EyeDiap; Table 4 shows K=4 gives the lowest angle error 5.05, so this value is selected to optimize the reported result.
  • tau_label (label confidence threshold) = 0.5
    Selected from {0.4,0.5,0.6} in Table 4 on EyeDiap; used in Eq. 10 to truncate label confidences before label correction.
  • tau_image (image confidence threshold) = 0.5
    Selected from {0.4,0.5,0.6} on EyeDiap; used to zero out low image confidences in sample weighting.
  • lambda (reconstruction regularization) = not reported
    Regularization coefficient in Eq. 1 for neighboring-label reconstruction weights; its value is never specified in the paper.
  • warm-up epochs = 10
    Warm-up duration before uncertainty estimation begins; chosen without a sensitivity analysis.
assumptions (4)
  • domain assumption Gaze labels are locally smooth in the encoder feature space, so a weighted average of the ground-truth labels of K same-person feature neighbors approximates the true label.
    Underpins the neighboring-label module; if false, TupleMD and TripleMD compare against a biased reference and label correction is corrupted. Invoked in the Neighboring Labeling Module and Eq. 4.
  • domain assumption The TupleMD and TripleMD values across the training set follow a bimodal Gaussian mixture separating reliable from unreliable samples.
    A two-component GMM is fitted and the smaller-mean component is treated as reliable; no evidence is given that the metrics are bimodal. Invoked in the Uncertainty Estimation Module.
  • domain assumption Restricting neighbors to the same person ID is sufficient to avoid identity-related confounds while still providing enough neighbors.
    KNN search is restricted to the same person ID; on small datasets this may limit neighbor quality or make K=4 unstable. Invoked in Algorithm 1 and the Neighboring Labeling Module.
  • domain assumption Exchanging corrected labels and weights between two networks prevents overconfidence and cumulative self-training errors.
    Borrowed from DivideMix and co-training literature; assumed rather than proven for gaze regression. Invoked in the Co-training Strategy section.

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Cite this review

Pith. "Pith review of Suppressing Uncertainty in Gaze Estimation." pith.science (2026). https://pith.science/paper/XWRP4HVS

@misc{pith2026241212890,
  author       = {Pith},
  title        = {Pith review of: Suppressing Uncertainty in Gaze Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XWRP4HVS}},
  note         = {Machine review of arXiv:2412.12890}
}
read the original abstract

Uncertainty in gaze estimation manifests in two aspects: 1) low-quality images caused by occlusion, blurriness, inconsistent eye movements, or even non-face images; 2) incorrect labels resulting from the misalignment between the labeled and actual gaze points during the annotation process. Allowing these uncertainties to participate in training hinders the improvement of gaze estimation. To tackle these challenges, in this paper, we propose an effective solution, named Suppressing Uncertainty in Gaze Estimation (SUGE), which introduces a novel triplet-label consistency measurement to estimate and reduce the uncertainties. Specifically, for each training sample, we propose to estimate a novel ``neighboring label'' calculated by a linearly weighted projection from the neighbors to capture the similarity relationship between image features and their corresponding labels, which can be incorporated with the predicted pseudo label and ground-truth label for uncertainty estimation. By modeling such triplet-label consistency, we can measure the qualities of both images and labels, and further largely reduce the negative effects of unqualified images and wrong labels through our designed sample weighting and label correction strategies. Experimental results on the gaze estimation benchmarks indicate that our proposed SUGE achieves state-of-the-art performance.

Figures

Figures reproduced from arXiv: 2412.12890 by the authors.

Figure 1
Figure 1. Illustration of uncertainties in gaze estimation us [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Visualization results of varying image and label [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. The pipeline of SUGE method. Initially, input images undergo feature extraction through the encoder, and a fully [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Visualization of samples with label confidences [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Continue with ORCID to comment.

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