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A contrastive loss that soft-weights every batch pair by rank gap learns ordered embeddings that set new marks on age, image, and video quality tasks.

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2026-07-10 12:55 UTC pith:K3VDOCHG

load-bearing objection Solid evolutionary method paper: soft all-pairs ordinal contrastive loss that cleanly beats RnC/GOL-style baselines under matched settings, with real multi-task evidence and code.

arxiv 2607.08109 v1 pith:K3VDOCHG submitted 2026-07-09 cs.LG

Contrastive Order Learning: A General Framework for Ordinal Regression

Chaewon Lee , BeomJun Shim , Kwang Pyo Choi , Chang-Su Kim This is my paper
classification cs.LG
keywords ordinal regressioncontrastive learningorder learningrank-aware embeddingsfacial age estimationblind image quality assessmentblind video quality assessment
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Ordinal regression needs embeddings that respect both order and distance between ranks, yet standard contrastive methods ignore magnitude of rank gaps and margin-based order methods only look at local pairs. ConOrd replaces hard positive/negative decisions with continuous affinity and disparity weights that depend on the squared rank difference of every pair in a batch, so every sample contributes to attraction or repulsion in proportion to how close its rank is to the anchor. The resulting loss, combined with a simple center regularizer, produces a globally consistent embedding space that can be read out by k-nearest neighbors. Across facial age estimation, blind image quality assessment, and blind video quality assessment the same objective consistently reaches or exceeds prior state-of-the-art numbers, showing that soft, all-pairs ordinal contrast is a practical general recipe for ordered prediction.

Core claim

Soft affinity and disparity weights derived from rank differences, when inserted into a batch-wise contrastive objective over all sample pairs, produce embeddings that simultaneously capture fine-grained ordinal structure and global consistency, outperforming both hard-threshold rank-aware contrastive losses and margin-based order learning on multiple ordinal regression benchmarks.

What carries the argument

The contrastive order loss L_ConOrd, which replaces binary positives/negatives with continuous weights a_ij = 1/((r_i-r_j)^2+ε) and b_ij = (r_i-r_j)^2 so every pair attracts or repels according to its rank gap.

Load-bearing premise

That simple quadratic functions of the rank gap are expressive enough for any rank range and label distribution without needing task-specific redesign.

What would settle it

Replace the quadratic affinity and disparity weights with linear or logarithmic alternatives (or remove soft weighting entirely) on the same backbones and splits; if the reported MAE/SRCC/PCC gaps on CLAP2015, BID, and LSVQ disappear, the soft-weight mechanism is not the source of the gains.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 5 minor

Summary. The paper proposes ConOrd, a contrastive framework for ordinal regression that replaces hard positive/negative assignment with a soft all-pairs contrastive order loss. Affinity weights a_ij = 1/((r_i-r_j)^2+ε) and disparity weights b_ij = (r_i-r_j)^2 modulate attraction and repulsion by rank gap; the resulting L_ConOrd is combined with a center loss and used with k-NN inference. The method is evaluated on facial age estimation, BIQA, BVQA, and additional regression/DIR benchmarks, with controlled loss ablations, gradient analysis, and public code.

Significance. If the empirical claims hold, ConOrd supplies a simple, general-purpose ordinal objective that unifies batch-level contrastive learning with rank-aware soft supervision and consistently improves over both classical order-learning (GOL) and hard-threshold rank contrastive (RnC) baselines. Strengths include multi-task SOTA tables (age, BIQA, BVQA), controlled same-backbone loss comparisons (Tables 4–6, 16–17), an explicit gradient derivation showing the attraction/repulsion condition (Appendix A), extensive hyperparameter and robustness checks, and released code. The contribution is primarily empirical and methodological rather than theoretical, but the breadth of validation and the soft all-pairs design are of clear practical value for ordinal representation learning.

minor comments (5)
  1. On CACD (Table 1) ConOrd is slightly behind NumCLIP (4.18 vs 4.11 MAE). A one-sentence discussion of when language-guided ordinal methods may still help would make the SOTA claim more precise.
  2. Section 3.2 and Appendix A correctly note that any monotonic a(d)/b(d) induces the desired crossover; a short forward pointer in the main text to Table 7 would help readers who stop at Eqs. (4)–(5).
  3. Inference relies on k-NN over the full training set (Eq. 10). Although Appendix C.5 reports low latency, a brief remark on scaling to very large training sets (or approximate NN) would be useful for practitioners.
  4. Notation for the batch size (2N) and the index set A(i) is standard but could be introduced once more cleanly when L_MAE is first written (Eq. 2).
  5. A few figure captions (e.g., Figure 1, Figure 4) are dense; expanding them slightly would improve readability without changing content.

Circularity Check

0 steps flagged

No significant circularity: empirical loss design evaluated on held-out benchmarks

full rationale

This is a standard empirical ML methods paper. The ConOrd loss (Eq. 3) is defined independently of the evaluation metrics (MAE, SRCC, PCC). Affinity and disparity weights (Eqs. 4–5) are chosen as simple monotonic functions of rank gap; Appendix A derives the gradient and shows that any a(d)/b(d) that decreases monotonically with d yields the desired attraction/repulsion crossover, while Tables 6/16/7 confirm that all eight monotonic configurations perform nearly identically and only non-monotonic/unconstrained variants degrade. Performance claims rest on controlled comparisons (Table 4), multi-task SOTA tables (1–3), public code, and extensive ablations on held-out test sets. Self-citations to prior order-learning work (GOL, etc.) appear only as baselines or related work, not as load-bearing uniqueness theorems or premises that force the result. No prediction is algebraically forced by a fitted constant that is then reported as a discovery. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 2 invented entities

The central claim rests on a small set of design choices (quadratic soft weights, temperature, center-loss regularizer, k-NN readout) plus standard deep-learning and ordinal-label assumptions. No new physical entities are postulated; the invented objects are the loss terms themselves. Free parameters are ordinary ML hyperparameters that are ablated but still chosen by hand.

free parameters (5)
  • temperature τ = 0.07
    Controls sharpness of the exponential kernel in L_ConOrd; fixed at 0.07 across most experiments after a sensitivity sweep.
  • affinity stabilizer ε = 1e-7
    Prevents division by zero in a_ij; set to 1e-7.
  • k for k-NN inference = dataset-dependent (Table 8)
    Number of neighbors used to average ranks at test time; chosen per dataset (e.g., 4–60).
  • functional form of a_ij and b_ij = a=1/(d^2+ε), b=d^2
    Quadratic inverse/quadratic forms are selected among monotonic candidates by ablation rather than derived from first principles.
  • L_ConOrd : L_center balance = 1 : 1
    Total loss uses equal weights by default; other ratios are ablated but the default is hand-chosen.
axioms (4)
  • domain assumption Rank labels induce a meaningful metric: attraction should decrease and repulsion increase monotonically with |r_i - r_j|.
    Stated as design requirement (i) in Section 3.2; underpins the entire soft-weight construction.
  • ad hoc to paper Batch-level all-pairs soft comparisons yield a more globally consistent ordinal embedding than local margin or hard-threshold pairs.
    Core modeling hypothesis motivating ConOrd versus GOL and RnC (Introduction and Section 3.2).
  • domain assumption ℓ2-normalized embeddings with κ_ij = -||z_i - z_j||^2 are an appropriate similarity for ordinal structure.
    Chosen after ablation (Table 6); standard in metric learning but not forced.
  • domain assumption k-NN averaging of training ranks is a valid readout for continuous or discrete ordinal prediction.
    Inherited from GOL (Lee et al., 2022) and used for all main results (Eq. 10).
invented entities (2)
  • Contrastive order loss L_ConOrd no independent evidence
    purpose: Replace hard positive/negative contrastive terms with soft rank-gap-weighted attraction and repulsion over all batch pairs.
    Defined in Eq. 3; the paper’s primary technical object.
  • Soft affinity and disparity weights a_ij, b_ij no independent evidence
    purpose: Modulate pairwise forces continuously by rank difference instead of binary thresholds or fixed margins.
    Instantiated in Eqs. 4–5; ablated but not independently measured outside the loss.

pith-pipeline@v1.1.0-grok45 · 37310 in / 2948 out tokens · 35759 ms · 2026-07-10T12:55:12.539852+00:00 · methodology

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read the original abstract

We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. While contrastive learning effectively leverages all samples in a batch, it typically ignores the inherent ordering among rank labels. Conversely, order learning explicitly models label ordinality but often relies on local, margin-based comparisons, limiting its ability to capture global ordinal structure. ConOrd addresses these limitations by introducing a contrastive order loss with soft affinity and disparity weights based on rank differences, enabling fine-grained modeling of ordinal relationships across all sample pairs within a batch. Extensive experiments on a range of ordinal regression tasks, including facial age estimation, blind image quality assessment, and blind video quality assessment, demonstrate that ConOrd consistently achieves state-of-the-art performance and generalizes well across diverse ordinal regression scenarios. The source code is available at https://github.com/cwlee00/ConOrd.

Figures

Figures reproduced from arXiv: 2607.08109 by BeomJun Shim, Chaewon Lee, Chang-Su Kim, Kwang Pyo Choi.

Figure 1
Figure 1. Figure 1: Comparison of three contrastive learning techniques: (a) supervised contrastive learning (SupCon) (Khosla et al., 2020), (b) Rank-N-Contrast (RnC) (Zha et al., 2023), and (c) the proposed ConOrd. SupCon considers the augmented view of an anchor as the only positive and treats all the others as negatives. RnC selects one sample as a positive and defines negatives as those with rank differences greater than … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed ConOrd framework for ordinal regression. is estimated by comparing it with reference instances of known ranks. For reliable reference selection, Lee & Kim (2021) decomposed object embeddings into order and iden￾tity features and selected references with similar identity features. Shin et al. (2022) proposed a regression-based for￾mulation to estimate a continuous relative rank betw… view at source ↗
Figure 3
Figure 3. Figure 3: The ConOrd loss LConOrd encourages the attraction of similar samples and the repulsion of dissimilar samples in the embedding space, by employing affinity weights aij and disparity weights bij , respectively. z = h(x) in the embedding space, where features are ℓ2- normalized to lie on the unit sphere. Suppose that a training set X contains M distinct ranks. We introduce M learnable reference points {µm}M m… view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of feature ordinality (Zha et al., 2023) on the CLAP2015 dataset. LSVQ-1080p, as well as cross-dataset evaluations on the remaining four datasets, are conducted. More details on the datasets are in Appendix B.3. Comparison with state-of-the-art methods [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of the losses in (2) and (3) on CLAP2015 and AgeDB-DIR [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Network architecture for BVQA. B.4. Training and inference configurations [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Examples of regression results on the SkyFinder dataset. The estimated and ground-truth values are specified under each image: estimated [true]. (a) Successful cases (b) Failure case pitch: -3.0 [1-2.0] yaw: -2.5 [-14.0] pitch: -12.0 [-19.5] yaw: 1 -5.0 [1-3.5] pitch:-8.0 [-8.0] yaw: -7.5 [-7.5] pitch: -12.0 [-12.0] yaw: 1 -5.0 [1-5.5] pitch: -12.0 [-11.0] yaw: -11.0 [-11.0] pitch: -5.0 [-5.0] yaw: -2.5 [-… view at source ↗
Figure 9
Figure 9. Figure 9: Examples of regression results on the MPIIFaceGaze dataset. The estimated and ground-truth values are specified under each image: estimated [true]. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Sensitivity of ConOrd to the temperature parameter τ on the BID dataset. Performance according to ϵ in (4) [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Sensitivity of ConOrd to ϵ on the BID dataset. Performance according to k in (10) [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of SupCon, RnC, and ConOrd under reduced supervision and test-time corruptions. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: t-SNE visualization of embeddings learned by SupCon, RnC, and ConOrd on the BID dataset. Colors denote MOS scores. Training stability and gradient dynamics [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Training loss and gradient-norm dynamics across different weighting configurations, showing stable convergence and controlled gradients in all cases. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: (a) Success and (b) failure cases of regression results on the facial age estimation datasets. Under each image, the estimated age is specified with the ground-truth in brackets. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: (a) Success and (b) failure cases of regression results on the BIQA datasets. Under each image, the estimated quality score is specified with the ground-truth in brackets. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: (a) Success and (b) failure cases of regression results on the BVQA datasets. Under each image, the estimated quality score is specified with the ground-truth in brackets. 30 [PITH_FULL_IMAGE:figures/full_fig_p030_17.png] view at source ↗

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Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages · 5 internal anchors

  1. [1]

    An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results

    Amig´o, E., Gonzalo, J., Mizzaro, S., and Carrillo-de Al- bornoz, J. An effectiveness metric for ordinal classifica- tion: Formal properties and experimental results.arXiv preprint arXiv:2006.01245,

    work page internal anchor Pith review Pith/arXiv arXiv 2006

  2. [2]

    SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation

    Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., and Spe- cia, L. SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation. InPro- ceedings of the 11th international workshop on semantic evaluation,

    work page 2017

  3. [3]

    J., Misevic, D., Steiner, U., and Guyon, I

    Escalera, S., Fabian, J., Pardo, P., Bar ´o, X., Gonz `alez, J., Escalante, H. J., Misevic, D., Steiner, U., and Guyon, I. ChaLearn looking at people 2015: Apparent age and cultural event recognition datasets and results. InProc. ICCV Workshops,

    work page 2015

  4. [4]

    QMamba: On First Exploration of Vision Mamba for Image Quality Assessment

    Guan, F., Li, X., Yu, Z., Lu, Y ., and Chen, Z. Q-Mamba: On first exploration of vision Mamba for image quality assessment.arXiv preprint arXiv:2406.09546,

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [5]

    Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning

    Liu, W., Ferstl, D., Schulter, S., Zebedin, L., Fua, P., and Leistner, C. Domain adaptation for semantic segmenta- tion via patch-wise contrastive learning.arXiv preprint arXiv:2104.11056,

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [6]

    SGDR: Stochastic Gradient Descent with Warm Restarts

    Loshchilov, I. and Hutter, F. SGDR: Stochastic gra- dient descent with warm restarts.arXiv preprint arXiv:1608.03983,

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [7]

    Decoupled Weight Decay Regularization

    Loshchilov, I. and Hutter, F. Decoupled weight decay regu- larization.arXiv preprint arXiv:1711.05101,

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    Pairs that are already close in the embedding space receive larger weights, while distant pairs are exponentially downweighted. Consequently, the gradient places greater emphasis on sample pairs that are both close in rank and close in the embedding space, enabling ConOrd to capture fine-grained ordinal distinctions more effectively. • Selective emphasis ...

    work page 2006

  9. [9]

    We adopt the evaluation protocol in (Gustafsson et al., 2020; Berg et al., 2021)

    • UTK (Zhang et al., 2017c): It consists of 20,000 facial images in a wide age range of [0,116]. We adopt the evaluation protocol in (Gustafsson et al., 2020; Berg et al., 2021). • CACD (Chen et al., 2015): It provides 160k images of 2000 celebrities, which have an age range of [14, 62]. We use the training set specified in (Shin et al., 2022). • Adience ...

    work page 2020

  10. [10]

    For data augmentation, only random horizontal flipping is applied

    to adjust the learning rate. For data augmentation, only random horizontal flipping is applied. B.2. Blind Image Quality Assessment Datasets:We evaluate the performance of the proposed ConOrd algorithm on the BIQA task using five datasets. For all datasets except FLIVE, we randomly split each dataset into train and test sets with a ratio of 4:1. Then, we ...

    work page 2023

  11. [11]

    Each image is assigned image attribute scores, but we use only the overall image quality scores in the range [0, 100]

    • SPAQ (Fang et al., 2020): Smartphone Photography Attribute and Quality (SPAQ) database includes 11,125 images taken with 66 different smartphones. Each image is assigned image attribute scores, but we use only the overall image quality scores in the range [0, 100]. • FLIVE (Ying et al., 2020): It is a large-scale BIQA dataset comprising approximately 40...

    work page 2020

  12. [12]

    For data augmentation, we use top-left, bottom-right, and center crops during training and use the average feature of the three cropped images

    with a 5-epoch warm-up phase, during which the learning rate increases gradually to five times the initial value. For data augmentation, we use top-left, bottom-right, and center crops during training and use the average feature of the three cropped images. 16 Contrastive Order Learning: A General Framework for Ordinal Regression B.3. Blind Video Quality ...

    work page 2021

  13. [13]

    All videos have a resolution of 540p

    to cover various contents and distortions. All videos have a resolution of 540p. • LIVE-VQC (Sinno & Bovik, 2018): It consists of 585 videos with various resolutions from 240p to 1080p. • CVD2014 (Nuutinen et al., 2016): It is composed of 234 videos in five distinct scene types, filmed using 78 different cameras. • YouTube-UGC (Wang et al., 2019): It prov...

    work page 2018

  14. [14]

    To enhance the temporal representations, we further refine the extracted temporal feature maps using a transformer module

    to extract temporal feature maps zP1 i , zP2 i , ..., zPM i from M sampled clips P 1 i , P 2 i , ..., P M i . To enhance the temporal representations, we further refine the extracted temporal feature maps using a transformer module. This module performs inter-frame and intra-frame attention for each temporal feature map, yielding refined temporal features...

    work page 2023

  15. [15]

    Table 9 shows that ConOrd achieves the best performance, outperforming all prior methods on both datasets

    as done in RnC (Zha et al., 2023). Table 9 shows that ConOrd achieves the best performance, outperforming all prior methods on both datasets. Table 9.Performance comparison on the SkyFinder and MPIIFaceGaze datasets. SkyFinder MPIIFaceGaze Algorithm MAE (↓) Angular MAE (↓) DEX (Rothe et al.,

    work page 2023

  16. [16]

    (a) Successful cases (b) Failure case 22℃ [34℃]1℃ [1℃] 11℃ [11℃] 17℃ [17℃] 21℃ [21℃] 30℃ [30℃] Figure 8.Examples of regression results on the SkyFinder dataset

    2.86 5.27 ConOrd(Proposed)2.65 4.98 Regression examples: We provide examples of regression results on the SkyFinder and MPIIFaceGaze datasets in Figure 8 and Figure 9, respectively. (a) Successful cases (b) Failure case 22℃ [34℃]1℃ [1℃] 11℃ [11℃] 17℃ [17℃] 21℃ [21℃] 30℃ [30℃] Figure 8.Examples of regression results on the SkyFinder dataset. The estimated ...

    work page 2018

  17. [17]

    The gradient norms differ moderately in scale but remain consistently bounded and settle quickly into steady ranges

    All configurations show smooth, monotonic decreases in training loss without signs of instability or divergence, indicating that the ConOrd formulation remains robust across a wide range of affinity-disparity designs. The gradient norms differ moderately in scale but remain consistently bounded and settle quickly into steady ranges. These results confirm ...

    work page 2000

  18. [18]

    Table 24.Compatibility of ConOrd with classification and regression heads. CLAP2015 BID LSVQ-test Method MAE (↓) SRCC (↑) PCC (↑) SRCC (↑) PCC (↑) LCE 2.605 0.742 0.762 0.902 0.901 LCE +L ConOrd +L center 2.599 0.876 0.8810.9040.903 LMAE 2.506 0.881 0.900 0.903 0.902 LMAE +L ConOrd +L center 2.481 0.899 0.914 0.904 0.904 Effectiveness as an auxiliary loss...

    work page arXiv

  19. [19]

    Table 31.Training time per epoch on BID and KonIQ-10k

    and is far more efficient than QCN (Shin et al., 2024), while scaling well to larger datasets. Table 31.Training time per epoch on BID and KonIQ-10k. DatasetL ConOrd LRnC QCN (Shin et al.,

    work page 2024