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REVIEW 3 major objections 5 minor 42 references

Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A single convolutional network trained from scratch on a new edge-detection dataset, BIPED, produces thin human-like edge maps and outperforms HED, RCF, CED, and BDCN on that benchmark.

desk verdict DexiNed contributes a genuinely useful dataset and a from-scratch edge detector, but the SOTA claims rest on a single test split with no error bars and some test-set tuning, so the abstract overstates what is shown. read the letter →

arxiv 1909.01955 v2 pith:22NP3K43 submitted 2019-09-04 cs.CV cs.LG

Xavier Soria , Edgar Riba , Angel D. Sappa This is my paper · ORCID
classification cs.CVcs.LG
keywords edgedetectionconvolutionalneuralnetworkdeeplearningdenseinceptionBIPEDdatasetmulti-scaleboundaryODS/OISF-measure
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

This paper proposes DexiNed, a deep convolutional network that predicts thin, human-plausible edge maps from color images. The authors claim that, trained only once from scratch on their newly introduced BIPED dataset, DexiNed beats four established deep edge detectors (HED, RCF, CED, BDCN) on BIPED under ODS/OIS F-measure and average precision, and generalizes to other edge-oriented datasets without fine-tuning. They also introduce BIPED, 250 carefully annotated outdoor images, as a public benchmark. The claim matters because it suggests a single, non-pretrained architecture can serve general edge detection, making the task simpler and more reproducible.

What carries the argument

The load-bearing design is DexiNed's encoder plus upsampling blocks. The encoder uses six main blocks inspired by Xception, with 3x3 convolutions, batch normalization and ReLU, plus two kinds of skip connections: main connections between blocks and edge connections that average sub-block outputs to preserve edge features in deep layers. Each block's features go through an upsampling block that gradually doubles resolution using transposed convolutions until the map matches the ground-truth size, producing eight intermediate edge-maps; these are fused by a learned layer. The network is deeply supervised with HED's weighted cross-entropy loss. This combination is what the paper credits for thin edges and for avoiding the edge-feature loss that DeepEdge identified in deep layers.

What would settle it

Run several independent random splits of BIPED into training and test sets, and hold out a separate validation set for all design choices, especially the upsampling method. If DexiNed's ODS/OIS advantage over RCF or BDCN disappears when re-tested on untouched splits, the reported generalization claim is not robust.

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Extended reading notes

Core claim

The central discovery claimed is that a densely connected, inception-style encoder with per-scale upsampling and deep supervision can learn edge detection from scratch, without ImageNet pretraining, provided the training set is carefully annotated. On their own BIPED test split, the averaged prediction (DexiNed-a) reaches ODS 0.859, OIS 0.867, and AP 0.905, above HED, RCF, CED, and BDCN trained on the same data. DexiNed also reports the best performance on MDBD, the other edge-oriented public dataset, while being trained only on BIPED. The qualitative claim that its edge maps are thin and visually coherent is supported by examples shown in the paper.

Load-bearing premise

The performance advantage rests on the assumption that the single random 200/50 split of BIPED, with no separate validation set, gives stable, unbiased F-measure estimates; the paper selected the upsampling strategy using the same test set, so the final numbers may be optimistically chosen.

Editorial extensions

If this is right

  • A single DexiNed trained once on BIPED can be dropped into edge-detection applications without per-dataset training or fine-tuning, at least for edge-oriented data.
  • The BIPED dataset gives the community a comparable benchmark where edge detectors can be trained from scratch and evaluated on ODS/OIS/AP.
  • Thin, visually plausible edge maps could improve downstream tasks like photo sketching, segmentation, or image-to-image translation.
  • Training from scratch removes the dependency on ImageNet-pretrained backbones for edge detection, enabling more compact or task-specific training pipelines.

Reading between the lines

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

  • If the BIPED benchmark is adopted, the field should also specify multiple random splits and a validation protocol, since the paper's single split may overstate small F-measure differences.
  • DexiNed's weaker results on BSDS500, NYUD, and PASCAL suggest the claims are specific to edge-detection datasets; users of boundary/contour benchmarks should not expect the same gains.
  • A quick test would compare DexiNed against RCF and BDCN when all are trained on BIPED and evaluated on an untouched random split and on a fourth edge dataset.
  • The no-fine-tuning generalization might depend on annotation density of the target dataset; test on sparsely annotated data with per-pixel precision/recall.
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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 DexiNed, a convolutional neural network for edge detection built from dense extreme inception blocks and trained from scratch on a newly introduced dataset, BIPED (Barcelona Images for Perceptual Edge Detection). The architecture produces six side outputs at different scales through learned upsampling blocks, and these outputs are combined into fused and averaged edge maps. Training uses a weighted cross-entropy loss (Eq. 1) with per-scale weights (Eq. 2). The authors evaluate DexiNed on BIPED, MDBD, CID, BSDS300, BSDS500, NYUD, and PASCAL, comparing against HED, RCF, CED, BDCN, and others. They claim superior ODS/OIS/AP performance on edge-detection-oriented benchmarks and that the model generalizes to other edge detection datasets without fine-tuning, while acknowledging that it underperforms on contour/boundary benchmarks where competing methods were trained on those datasets.

Significance. If the empirical claims are accepted, the paper makes two useful contributions: a new from-scratch architecture that avoids ImageNet pretraining and produces thin edge maps, and a new carefully annotated dataset, BIPED, with released code and data. Those strengths are real and should be credited. However, the central performance claims are empirical architecture claims, not derivations, and they currently rest on a single random split of BIPED, on design choices made using the test set, and on a very small cross-dataset margin on MDBD. The paper would be significantly strengthened by repeated-split evaluation, confidence intervals, and clearer separation between model selection and evaluation. With those additions, the contribution would be solid; without them, the reported margins over RCF and BDCN are not yet established.

major comments (3)
  1. [§4.1, §5.1, Table 1(b)] The reported state-of-the-art margins on BIPED are not statistically supported because the model is selected using the test set. Section 5 states that the architecture was 'carefully tuned till reach its final version,' and Section 5.1 chooses the upsampling strategy by comparing precision/recall curves on the same 50-image test set (Fig. 6(a)). Since the final DexiNed-a and DexiNed-f results are therefore selected, not predicted, on this test set, the ODS differences in Table 1(b) (DexiNed-a .859 vs. RCF .843, BDCN .839, HED .829, CED .795) may be optimistically biased. The authors should use a held-out validation split for all design choices or report results over multiple random BIPED splits with paired confidence intervals; without this, the claim that DexiNed systematically outperforms prior methods on this benchmark is not established.
  2. [§5.1, Table 2 (MDBD)] The cross-dataset generalization claim rests on a difference that is within noise. In Table 2, DexiNed-a achieves ODS .859 on MDBD versus RCF .857 on the 20-image MDBD test subset, a 0.002 margin, and the text states that 'DexiNed obtains the best performance in the MDBD dataset' even though the table reports DexiNed-f at .837/.837. With no significance test or confidence interval over the 20 test images, and with internally inconsistent numbers for DexiNed-f, the claimed improvement over RCF/HED on MDBD is not demonstrated. The authors should report per-image paired differences, bootstrap intervals, or repeated evaluation protocols, and should reconcile the DexiNed-f entries.
  3. [Abstract, §5.1, Table 2] The abstract's claim that evaluations on 'different benchmarks' show improvements is overbroad. Table 2 shows that DexiNed substantially underperforms methods trained on the corresponding data on BSDS500 (ODS .728 vs. RCF .806), NYUD (.602 vs. HED .720), and PASCAL (.475 vs. CED .726); the authors themselves attribute this to task mismatch. The improvement claim should be restricted to the edge-detection-oriented settings (BIPED and MDBD), and the paper should explicitly state that on contour/boundary benchmarks the proposed method is not competitive.
minor comments (5)
  1. [§3.3, Eq. (2)] In Eq. (2), the notation is inconsistent: $L(W,w)$ and $w_n$ are used without clearly defining the subscript on $w$; the summand in Eq. (1) uses the unusual symbol $\wr_n$ instead of a standard loss notation. Please clarify the variables.
  2. [§4.1] The augmentation description says the process 'resulted in 288 images per each 200 images,' which is unclear; please clarify whether 288 is the total augmented set per original image or per training subset, and correct the sentence.
  3. [§4.1, §4.2] The dataset name is written as 'MDBM' in Section 4.1 and as 'MDBD' elsewhere; please use the acronym consistently throughout.
  4. [§4.4] The fusion-layer initialization is described as $1/(N-1)$ without specifying the number of outputs $N$ or the rationale; please give the exact initialization and verify the denominator.
  5. [Throughout] There are several typos and inconsistencies: 'CCNs' in the Introduction, 'Dixe' in Section 4.4, and mixed capitalization of 'xception'/'Xception'. A careful proofreading pass is recommended.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: DexiNed is an empirical architecture evaluated on held-out and external datasets, with no fitted constants repackaged as predictions.

full rationale

The paper does not derive any result from an equation that presupposes the conclusion. Its contributions are a CNN architecture (DexiNed), a new annotated edge dataset (BIPED), and empirical evaluations against existing edge detectors. The reported BIPED F-measures come from a test set of 50 images separated from the 200 training/validation images (Sec. 4.1), and the cross-dataset results (MDBD, CID, BSDS, NYUD, PASCAL) are obtained by a model trained only on BIPED and evaluated on images the model never saw (Sec. 5.1, Table 2). No parameter is fitted to the reported test labels and then renamed as a prediction; no uniqueness theorem or self-citation is invoked to force the architecture choice. The selection of the DexiNed-dc upsampling variant using test-set precision/recall curves (Fig. 6(a)) is a potential source of optimistic bias in the magnitude of the reported margins, but that is an experimental-design concern, not circularity: the final numbers still come from held-out test images and the qualitative conclusions do not reduce to the selection criterion by construction. Accordingly, the circularity score is 0.

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

The paper makes no first-principles derivation. The central claim is empirical, so the ledger records the architecture and training parameters that were chosen by hand or search, plus the domain assumptions that make the evaluation protocol meaningful.

free parameters (5)
  • Per-scale loss weights delta_n = not reported
    Eq. 2 weights each intermediate edge-map loss in the total loss; no values are given, but they affect training and final edge maps.
  • Number of filters per block = e.g., 256, 512 depending on block
    Architecture design; the paper says hyperparameter search reduced the parameter count (Sec 4.4), but the search results are not tabulated.
  • Kernel sizes (3x3, 1x1, sxs) = 3x3, 1x1, sxs per block
    Chosen by 'best performance' after hyperparameter search (Sec 4.4).
  • Upsampling strategy = DexiNed-dc: transposed convolution with trainable kernels
    Selected empirically based on test-set precision/recall curves (Fig. 6a, Sec 5.1).
  • Data augmentation parameters = 15 rotations, horizontal flip, gamma 0.3030 and 0.6060, half-width split
    Chosen by hand (Sec 4.1); affects training distribution and final model.
assumptions (4)
  • domain assumption BIPED annotations are correct ground truth for edge detection
    Training and evaluation depend on annotation correctness; no inter-annotator agreement or independent validation is reported (Sec 4.1).
  • domain assumption Edge detection and contour/boundary detection are distinct tasks that should be evaluated separately
    This assumption justifies disregarding or explaining away lower scores on BSDS, NYUD, PASCAL, and CID (Sec 5.1, 5.2).
  • domain assumption ODS/OIS/AP F-measures are the appropriate and sufficient evaluation metrics
    Standard metrics (Sec 4.3), but the choice affects rankings; e.g., per-image best threshold (OIS) may favor methods that overproduce edges.
  • domain assumption The single random 200/50 split of BIPED is representative and stable
    No multiple splits or repeated runs are reported; statistical stability is assumed (Sec 4.1).

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Cite this review

Pith. "Pith review of Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection." pith.science (2026). https://pith.science/paper/22NP3K43

@misc{pith2026190901955,
  author       = {Pith},
  title        = {Pith review of: Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/22NP3K43}},
  note         = {Machine review of arXiv:1909.01955}
}
read the original abstract

This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered.

Figures

Figures reproduced from arXiv: 1909.01955 by the authors.

Figure 1
Figure 1. The edge-maps predictions from the proposed model in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Edge-maps predicted from the state-of-the-art models and DexiNed on three BSDS500 [ [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Proposed architecture: Dense Extreme Inception Network, consists of an encoder composed by six main blocks (showed in light [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Detail of the upsampling block that receives as input the [PITH_FULL_IMAGE:figures/full_fig_p003_4.png]
Figure 5
Figure 5. Figure 5: Edge-maps from DexiNed in BIPED test dataset. The six outputs are delivered from the upsampling blocks, the [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Precision/recall curves on BIPED dataset. (a) DexiNed upsampling versions. (b) The outputs of DexiNed in testing stage, the 8 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Results from different edge detection algorithms trained and evaluated in BIPED dataset. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Results from the proposed approach using different datasets (note that DexiNed has been trained just with BIPED). [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

Discussion (0). Continue with ORCID to comment.

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