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REVIEW 2 major objections 7 minor 54 references

GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification

T0 review · 2 major / 7 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A grayscale twin stream beats an RGB-only twin for person re-identification.

desk verdict The RGB-grey versus RGB-RGB comparison is the one genuinely new piece here, and the paper's ablations are careful, but the central claim rests on single-run margins that are within Re-ID training noise. read the letter →

arxiv 1908.05142 v2 pith:YICUNLQJ submitted 2019-08-14 cs.CV

Lei Qi , Lei Wang , Jing Huo , Yinghuan Shi , Yang Gao This is my paper · ORCID
classification cs.CV
keywords personre-identificationtwo-streamnetworkgrayscaleimagescolorover-fittingfeaturefusiontripletlossMarket1501MSMT17
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 argues that color is a double-edged cue in person re-identification: most false matches in a ranking list share color with the query, so RGB models over-fit to color and miss structure and texture. It claims that RGB and grayscale views are complementary, because a model trained on grayscale images recovers many hard cases that an RGB model misses. To exploit this, GreyReID runs the same image through a two-stream ResNet-50, one stream on RGB and one on grayscale, merges the two at a joint branch, and concatenates all three features as the final descriptor. On Market1501, DukeMTMC-reID, CUHK03-NP, and MSMT17, the RGB+grayscale input pair consistently beats the same architecture using two RGB streams, and the full method outperforms the compared state of the art. If true, the finding offers a parameter-free way to reduce color over-fitting with no extra data or models.

What carries the argument

The load-bearing mechanism is a two-stream deep network whose RGB and grayscale branches are fused before pooling. Each branch is a pretrained ResNet-50; the grayscale input is generated by the weighted conversion $Grey(i,j)=0.299R(i,j)+0.587G(i,j)+0.114B(i,j)$ and replicated to three channels. The two branches' last convolutional tensors are combined by element-wise addition into a joint tensor, which is split into two non-overlapping parts before global average pooling, and the three resulting 256-, 512-, and 512-dimensional features are concatenated into a 1280-dimensional descriptor. Each branch is supervised independently by a cross-entropy loss plus a hard-mining triplet loss, and the concatenated feature receives a global loss, so the framework jointly learns to trust color, structure, and their interaction.

What would settle it

Rerun the Table 8 comparison ten times with different random seeds on MSMT17, where the reported RGB-grey advantage is largest (1.4 mAP), and check whether the paired difference is consistently positive across runs; if the advantage is smaller than the run-to-run standard deviation, the claim that grayscale input improves person re-identification is not supported.

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Extended reading notes

Core claim

The central claim is that removing color information helps person re-identification when the grayscale view is combined with the RGB view through an explicitly fused architecture, rather than used as ordinary data augmentation. The paper supports this with three observations: RGB models lose over 44% mAP when tested on grayscale images, a grayscale-trained model recovers much of that gap, and the two models' feature response maps attend to different regions. Its network feeds RGB and grayscale versions of each image into two ResNet-50 streams, sums the last-convolution tensors into a joint branch, and concatenates the branch features; each branch is trained with its own cross-entropy plus triplet loss, and a global loss tunes the concatenation. The decisive comparison in Section 4.5 uses identical settings and reports that RGB-grey inputs outperform RGB-RGB inputs on all four benchmarks, by 0.5, 0.8, 0.3, and 1.4 mAP respectively. Greyscale images used as plain data augmentation, by contrast, degrade or fail to improve performance, which the paper takes as evidence that the fusion architecture is what extracts the value.

Load-bearing premise

The load-bearing premise is that the small, consistent improvements of the RGB-grey pair over the RGB-RGB pair are genuine training effects and not run-to-run noise, because each configuration is evaluated a single time without reported variance.

Editorial extensions

If this is right

  • A cheap preprocessing step—converting RGB images to grayscale in each batch—adds a complementary stream that improves mAP and Rank-1 on all four benchmarks, with the largest gain on the largest dataset, MSMT17.
  • The per-branch and global losses are necessary; removing branch losses drops mAP substantially (85.6 to 79.1 on Market1501), showing the architecture benefits from independent supervision of each modality.
  • Simply mixing grayscale images into the training set as augmentation does not help (Table 13) and often hurts; the fusion into a joint branch is what captures the complementarity.
  • The framework generalizes across backbones: switching ResNet-50 to ResNet-101 raises mAP on all four datasets, for example from 55.0 to 59.1 on MSMT17.
  • Because the grayscale conversion is a fixed pixel operation that requires no extra training data or generative models, the approach can be attached to existing part-based or attention-based Re-ID backbones.

Reading between the lines

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

  • Our inference: the consistent RGB-grey advantage over RGB-RGB suggests the grayscale stream acts as a regularizer against color over-fitting, and the same idea could be tested in other retrieval tasks where color is dominant but noisy, such as vehicle re-identification.
  • Our inference: since the paper runs each configuration once, practitioners should rerun the RGB-grey versus RGB-RGB comparison with multiple seeds before treating the 0.3–1.4 mAP gaps as deployment guarantees; the reported pattern is directionally consistent but variance is unreported.
  • A testable extension the paper does not explore is replacing the grayscale stream with other color-invariant views, such as Lab lightness or an adversarially color-perturbed stream, to see whether the benefit comes specifically from grayscale or from any view that suppresses color.
  • The two-stream idea could be adapted to RGB-infrared cross-modality Re-ID by using the grayscale branch as a shared intermediate representation, although the paper explicitly distinguishes its same-modality task from cross-modality matching.
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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

2 major / 7 minor

Summary. The paper proposes GreyReID, a two-stream deep framework for person re-identification that jointly exploits RGB and grayscale images. RGB images are converted to grayscale using a standard luminance formula; the two modalities are processed by separate ResNet-50 branches, fused into a joint branch with part-based pooling, and the features of all three branches are concatenated for retrieval. Training uses per-branch and global losses combining cross-entropy and hard-mining triplet loss. Experiments on Market1501, DukeMTMC-reID, CUHK03-NP, and MSMT17 report gains over a single-stream baseline, comparisons with state-of-the-art methods, and an ablation showing that the RGB-grey input pair outperforms an RGB-RGB pair in the same architecture. The central claim is that RGB and grayscale information are complementary for person Re-ID.

Significance. If the empirical claim holds, the paper makes a simple and practical contribution: it identifies color over-fitting as a failure mode in Re-ID and shows that a conversion-based grayscale stream, requiring no extra annotations or learned generation, can improve accuracy when fused with the RGB stream. The experimental design is commendable: the RGB-RGB pair condition isolates the effect of the grayscale input by keeping the architecture and training pipeline fixed, and the results are directionally consistent across four benchmarks. The paper is also transparent in reporting ablations over loss components, fusion schemes, feature dimensions, backbones, and conversion methods, and it does not rely on fitted constants or circular derivations. However, the magnitude of the central effect (0.3–1.4 mAP) is small relative to typical training noise, and the paper provides no error bars, seeds, or significance tests, which currently limits the strength of the conclusion.

major comments (2)
  1. [Section 4.5, Table 8] The central claim that RGB-grey input is complementary to RGB input rests entirely on the comparison between the 'RGB-RGB pair' and 'RGB-grey pair' rows in Table 8, where the gains are 0.5/0.8/0.3/1.4 mAP and 0.5/0.5/0.9/1.3 Rank-1 on Market1501/Duke/CUHK03/MSMT17. The paper reports only single-run results with no standard deviations, no seeds, and no significance tests, and these margins are within the run-to-run variance commonly observed in deep Re-ID training. As written, the evidence does not rule out the possibility that the apparent improvement from grayscale is noise; the authors should report mean and standard deviation over at least five independent runs with fixed seeds and apply a paired significance test to the RGB-grey versus RGB-RGB comparison.
  2. [Section 4.4, Table 4; Section 4.5, Table 8] The manuscript contains an internal inconsistency that bears directly on the central claim. The 'Two-part Grey+RGB+Joint' row in Table 4 should describe the same configuration as the 'RGB-grey pair' row in Table 8 (two-part architecture with all three branch features concatenated), yet the reported numbers differ on every dataset: Market1501 85.3 versus 85.6 mAP, Duke 75.9 versus 76.5, CUHK03 69.6 versus 69.9, MSMT17 54.5 versus 55.0. In addition, Table 12 lists '256+512+512=1280' with 86.0 mAP on Market1501, while the main results report 85.6 for the same nominal feature configuration. The authors need to state explicitly whether these are different runs, different hyperparameters (e.g., λ or feature dimensions), or different data splits, and to make the configuration used for each reported number unambiguous; the observed gaps are of the same order as the claimed RGB-grey advantage and currently undermine the reliability of the comparison.
minor comments (7)
  1. [Section 4.3, Table 1] The table header contains the typo 'Mehods'; please change to 'Methods'. Also, 'CHUK03' in the captions of Tables 4, 6, and 8 should be 'CUHK03'.
  2. [Section 4.2] The phrase 'This can be excerpted because' should read 'This can be expected because'.
  3. [Section 4.5] In the discussion of Table 9, 'smilar' and 'RBG' should be 'similar' and 'RGB'; additionally, the sentence 'as a RBG image does' is grammatically unclear.
  4. [Section 3.3] The heading 'The different between the proposed method and the cross-modal method' should be 'The difference between the proposed method and cross-modal methods'.
  5. [Section 4.1] It would help reproducibility to state whether random erasing is applied before or after the RGB-to-grayscale conversion, and whether the grayscale branch receives three replicated channels (as mentioned in Section 3.1) with the same normalization statistics as the RGB branch.
  6. [Section 4.5, Fig. 3] The caption of Fig. 3 is confusing: the parenthetical color descriptions for the bars (orange, grey, blue) do not match the visible bar labels in the figure, and the sentence 'Blue bars represent using RGB and greyscale images to train and test the ResNet-50 model, respectively' should be reworded to identify which model is evaluated on which modality.
  7. [Section 4.3] The state-of-the-art comparison tables list mostly methods from 2018 and earlier; to support the 'state-of-the-art' claim, the authors should add contemporary baselines or soften the wording in Section 4.3.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: GreyReID's claims rest on held-out benchmark experiments, not on self-citation or fitted inputs.

full rationale

The paper's central claim is empirical: that RGB and greyscale inputs are complementary for person Re-ID, demonstrated by comparing the same two-stream architecture with RGB-grey versus RGB-RGB inputs (Section 4.5, Table 8) and by ablation studies (Table 4). The greyscale conversion in Eq. (1) is a standard luminance transform with fixed coefficients, and no parameter is fit to the test-set metrics. Hyperparameters such as lambda, margin, and feature dimensions are tuned or ablated on validation experiments (Tables 7 and 12), which is ordinary model selection, not circular reasoning. Self-citations appear only in the related-work discussion (references [20,21,22]) and are not load-bearing for the proposed method or its evaluation. The claimed improvement of RGB-grey over RGB-RGB is a direct experimental comparison on held-out benchmark splits, not an identity by construction. The noted inconsistency between Table 4's 'Grey+RGB+Joint' row and Table 8's 'RGB-grey pair' row is a reproducibility or statistical-power concern about single unseeded runs, but inconsistency is not circularity. No derivation step in this paper reduces, by the paper's own equations or by self-citation, to its own inputs.

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

The paper contributes an architecture and an empirical claim, not a derivation. The free parameters are training hyperparameters selected on validation data. There are no invented physical entities. The main unstated costs are standard domain assumptions about transfer learning, loss design, and benchmark validity.

free parameters (5)
  • lambda (loss trade-off weight) = 1.0
    Weights cross-entropy and triplet losses in Eq. (5); Table 7 sweeps values from 0 to 2.0 and selects 1.0 based on validation performance.
  • Triplet margin m = 0.3
    Fixed in Section 4.1 for hard-mining triplet loss; no ablation is reported for this value.
  • Batch sampling P and K = P=32, K=4
    Number of persons and images per person in each batch; chosen in Section 4.1 as standard practice without ablation.
  • Branch feature dimensions = 256 (grey), 512 (RGB), 512 (joint)
    Feature dimensionalities for the three branches; Table 12 evaluates several combinations and this one is used for the main results.
  • Number of joint parts = 2
    The joint tensor is split into two non-overlapping sub-tensors; one-part versus two-part is ablated, but only the value 2 is used in the main model.
assumptions (4)
  • domain assumption ImageNet-pretrained ResNet-50 provides a transferable feature backbone for person images.
    All branches initialize from ImageNet weights (Section 3.2), a standard but unproved assumption in this domain.
  • domain assumption Combining cross-entropy classification with hard-mining triplet loss is an effective training objective for Re-ID.
    Adopted from prior work and used for every branch in Eq. (5); it is not derived in this paper.
  • domain assumption Luma grayscale conversion preserves the structure and texture information needed to complement color cues.
    Eq. (1) defines the conversion and the greyscale image is replicated to three channels, but the paper does not formally justify that useful non-color information survives.
  • domain assumption The four benchmark datasets and CMC/mAP metrics are representative of real person Re-ID performance.
    Evaluation in Section 4 follows standard practice for the field, but the paper provides no statistical analysis of variance across runs or datasets.

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Cite this review

Pith. "Pith review of GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification." pith.science (2026). https://pith.science/paper/YICUNLQJ

@misc{pith2026190805142,
  author       = {Pith},
  title        = {Pith review of: GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YICUNLQJ}},
  note         = {Machine review of arXiv:1908.05142}
}
read the original abstract

In this paper, we observe that most false positive images (i.e., different identities with query images) in the top ranking list usually have the similar color information with the query image in person re-identification (Re-ID). Meanwhile, when we use the greyscale images generated from RGB images to conduct the person Re-ID task, some hard query images can obtain better performance compared with using RGB images. Therefore, RGB and greyscale images seem to be complementary to each other for person Re-ID. In this paper, we aim to utilize both RGB and greyscale images to improve the person Re-ID performance. To this end, we propose a novel two-stream deep neural network with RGB-grey information, which can effectively fuse RGB and greyscale feature representations to enhance the generalization ability of Re-ID. Firstly, we convert RGB images to greyscale images in each training batch. Based on these RGB and greyscale images, we train the RGB and greyscale branches, respectively. Secondly, to build up connections between RGB and greyscale branches, we merge the RGB and greyscale branches into a new joint branch. Finally, we concatenate the features of all three branches as the final feature representation for Re-ID. Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch. Besides, a global loss function is utilized to further fine-tune the final concatenated feature. The extensive experiments on multiple benchmark datasets fully show that the proposed method can outperform the state-of-the-art person Re-ID methods. Furthermore, using greyscale images can indeed improve the person Re-ID performance.

Figures

Figures reproduced from arXiv: 1908.05142 by the authors.

Figure 1
Figure 1. Person retrieval samples of the PCB model [ [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Person retrieval samples of greyscale and RGB images in the PCB model [ [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The evaluation of the greyscale and RGB models on Market1501, DukeMTMC-reID and CUHK03, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Feature response maps of the last convolution layer in the greyscale and RGB models on CUHK03. The [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Illustration of the proposed two-stream deep framework with RGB-grey information. First, we develop [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Comparison between our task and the cross-modality task in [ [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Comparison of the baseline and our method on Market1501, DukeMTMC-reID and CUHK03-NP, [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: As seen, we can observe that our proposed method can almost converge after 200 epochs. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 8
Figure 8. Figure 8: Convergence curves of the proposed method on MSMT17. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: The numbers of cameras and images on the four benchmark datasets. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Feature response maps of the last convolutional layer in the RGB and greyscale branches of the [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.