Pith. sign in

REVIEW 3 major objections 7 minor 1 cited by

ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal Dissection

T0 review · 3 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A regression network predicts safety margins for robotic endoscopic dissection with a mean absolute error of 3.18, enabling intraoperative visual guidance.

desk verdict The ETSM dataset is the real contribution, but the confidence-map label definition is broken as written and the headline MAE is not interpretable until it is fixed. read the letter →

arxiv 2411.18884 v1 pith:VOB5ND7N submitted 2024-11-28 cs.RO cs.CV

Mengya Xu , Wenjin Mo , Guankun Wang , Huxin Gao , An Wang , Long Bai , Chaoyang Lyu , Xiaoxiao Yang
show 2 more authors
Zhen Li Hongliang Ren
This is my paper · ORCID
classification cs.ROcs.CV
keywords endoscopicsubmucosaldissectionrobot-assistedsurgerytrajectorypredictionsafetymarginconfidencemapregressionnetworkDINOv2behaviorcloning
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 aims to make robot-assisted endoscopic submucosal dissection (ESD) safer by giving the surgeon, or an autonomous system, two kinds of intraoperative guidance: a suggested dissection trajectory and a confidence map that grades the dissection area by safety margin. To do this, the authors create the ETSM dataset of 1,849 short clips from dual-arm robotic ESD on ex-vivo porcine models, with annotations for optimal trajectories and safety margins. They define a confidence value at each point in the dissection area as the ratio of its distance to the safety margin over its distance to the optimal trajectory, generated by an angular-difference-based algorithm that avoids picking the wrong side of a curved margin. On this task, their proposed regression network RCMNet, which combines a DINOv2 encoder fine-tuned with LoRA and an All-MLP decoder, reaches a mean absolute error of 3.18 at $532\times532$ resolution, lower than the crowd-counting-derived baselines MAN and SASNet. For trajectory suggestion, behavior cloning outperforms two diffusion-based methods, and the paper frames the regression formulation as a better fit for safety-margin delineation than segmentation.

What carries the argument

The load-bearing machinery is the confidence-map generation rule of Eq. (1): for each point in the dissection area, the confidence is the ratio of its distance to the nearest safety-margin point over its distance to the nearest trajectory point, $\frac{\sqrt{(x_e-x_a)^2+(y_e-y_a)^2}}{\sqrt{(x_t-x_a)^2+(y_t-y_a)^2}}$. Because a naive nearest-margin search can pick the wrong side of a curved margin, the paper adds an angular-difference search: find the nearest trajectory point, cast a ray from it through the area point, then select the margin point whose direction has the smallest angular difference from that ray; a distance threshold is introduced for curved cases. The second pillar is RCMNet, which uses a pre-trained DINOv2 ViT-B/14 encoder with LoRA fine-tuning, extracts multi-scale features from transformer blocks 3, 6, 9, and 12, and decodes them with an All-MLP network (channel unification, upsampling, concatenation, fusion MLP, and a 1-channel prediction head). Training uses a weighted MSE loss with weight 10 on pixels outside the safety margin, so the model learns to output zero confidence there. The trajectory-suggestion comparisons are carried out with behavior cloning, MID, and iDiff-IL.

What would settle it

Recompute the ETSM ground-truth labels by explicitly clamping or normalizing the Eq. (1) ratio to $[0,1]$, or by specifying the missing distance threshold, and re-run the RCMNet versus MAN/SASNet comparison. If the reported MAE of 3.18 at $532\times532$ shifts materially, or if the relative ordering of the models changes, the paper's central quantitative claim is not stable under the correct label definition. An even more direct check is to calculate the fraction of labeled pixels whose raw confidence exceeds 1 in the released dataset; a non-negligible fraction would confirm that the ground truth is not a proper confidence field.

Watch

Extended reading notes

Core claim

The paper's central claim is that the safety margin of an endoscopic submucosal dissection area can be represented as a continuous confidence field — value 1 on the optimal dissection trajectory, 0 on and outside the safety margin, smoothly decreasing in between — and that this field can be predicted directly by a regression network. On the ETSM test set, their RCMNet reaches a mean absolute error of 3.18 and a mean squared error of 344.40 at $532\times532$ resolution, beating the crowd-counting baselines MAN (MAE 3.68) and SASNet (MAE 4.19), and it also attains the lowest mean MAE under the four corruption categories. Qualitatively, the predicted maps track the gradient from trajectory to margin even in difficult cases, and the method is also applied to out-of-domain live porcine and patient frames. In the trajectory-suggestion half of the framework, behavior cloning with a CNN-MLP gives average displacement error 9.10, final displacement error 11.20, and Fréchet distance 20.14, outperforming MID and iDiff-IL. The authors frame these results as the first regression-based approach for delineating varying safety levels of dissection areas.

Load-bearing premise

The load-bearing premise is that the ground-truth confidence maps are a well-defined field whose values lie in $[0,1]$; the paper defines confidence as a distance ratio that can exceed 1 and introduces a distance threshold for curved cases without stating how the ratio is clamped or normalized.

Editorial extensions

If this is right

  • RCMNet's predicted confidence maps can be overlaid on the endoscopic view in real time, giving the surgeon a graded 'safe–caution–danger' display during dissection.
  • The angular-difference-based confidence generation handles curved dissection areas by avoiding opposite-margin misassignment, a property the authors demonstrate in Fig. 2.
  • The regression formulation for safety-margin prediction outperforms the segmentation formulation, since segmentation methods achieve only roughly 0.45 IoU and are deemed unreliable for this task.
  • Behavior cloning's trajectory suggestions (ADE 9.10) are accurate enough to serve as a starting point for automated dissection subtasks, though full autonomy is not claimed.

Reading between the lines

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

  • Because the confidence value is a dimensionless pixel-distance ratio, converting it to physical units by calibrating pixel size from the endoscope's optics would make the reported MAE interpretable as a true margin error in millimeters; the paper does not perform that calibration.
  • The current confidence module uses a single input frame while the trajectory module uses three frames; fusing temporal information into the confidence branch is a natural extension the paper lists as future work and is likely to reduce errors during tool-induced occlusions.
  • The dataset comprises 21 ex-vivo porcine procedures with annotations by expert endoscopists; a multi-expert study measuring inter-annotator agreement on trajectory and margin placement would clarify how much of the 3.18 MAE is annotation noise rather than model error.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 7 minor

Summary. The manuscript introduces ETSM, a dataset of 1,849 short clips from ex-vivo porcine robotic endoscopic submucosal dissection (ESD), with annotations of dissection trajectories and safety margins, along with an angular-difference algorithm to generate confidence-map safety margins. The authors propose RCMNet, a DINOv2-encoder plus All-MLP-decoder regression network, to predict these confidence maps, and evaluate trajectory suggestion using behavior cloning (BC), motion indeterminacy diffusion (MID), and implicit diffusion policy imitation learning (iDiff-IL). The central quantitative claims are that BC achieves ADE 9.10 for trajectory suggestion and that RCMNet achieves MAE 3.18 for confidence-map prediction at 532x532 resolution, outperforming MAN and SASNet in in-domain and robustness evaluations. The out-of-domain evaluation is qualitative, showing predictions on live porcine and patient data.

Significance. If the confidence-map formulation is made well-defined and reproducible, the paper would contribute a useful dataset and a novel regression-based approach for safety-margin visualization in robot-assisted ESD. Strengths include the release of dataset and code, the use of a video-level train/test split, the inclusion of robustness evaluation under several corruption types, and the first application of a regression decoder with a pre-trained DINOv2 encoder to this task. The trajectory-suggestion comparison with three baselines is a reasonable contribution. However, the central quantitative claim—RCMNet's MAE of 3.18—depends entirely on the ground-truth confidence map definition, which as written is internally inconsistent and not a proper [0,1] confidence field. The concern raised in the stress-test note directly lands on this manuscript: Eq. (1) does not produce bounded confidence values and the text itself acknowledges that values exceed 1. This must be resolved before the reported numbers can be interpreted.

major comments (3)
  1. [Section II-A, Eq. (1)] The definition of the ground-truth confidence map is not well-defined. The text assigns confidence 1 to the dissection trajectory and 0 to the safety margin and its exterior, but Eq. (1) computes C = d_margin / d_trajectory. At a point on the trajectory, the denominator is zero, making C undefined or infinite; at the margin, C = 0. The sentence 'the confidence exceeds 1' in the following paragraph confirms that values are not clamped to [0,1]. Since the weighted MSE loss in Eq. (2) and all MAE/MSE values in Table I are computed against these labels, the reported MAE of 3.18 is not interpretable as an error on a confidence map. Please state the exact label-generation formula actually used (e.g., d_margin / (d_margin + d_trajectory)), or describe and justify a clamping/normalization step, and regenerate all reported results with the corrected formulation.
  2. [Section II-A, distance threshold] The 'distance threshold between the margin point and the area point' is introduced to handle curved dissection areas, but its value is never specified. Because this threshold directly affects which margin point is selected and hence the resulting confidence values, every number in Table I depends on this unspecified hyperparameter. This makes the algorithm non-reproducible. Please provide the threshold value, the units (pixels or millimeters), and the exact selection rule (e.g., the margin point with the smallest angular difference among those within T pixels).
  3. [Section III-C-2, Table I] The evaluation reports single MAE/MSE values per model with no standard deviations, confidence intervals, or repeated-seed experiments. The test set consists of 369 clips from four videos, so the observed difference between RCMNet (MAE 3.18) and SASNet (MAE 4.19) could be within run-to-run variability. Please report results over multiple seeds or provide a statistical test, and specify the numerical range of the confidence maps used in the metrics (e.g., 0–1 or 0–255) so that the MAE values are interpretable.
minor comments (7)
  1. [Section II-B-2-a] The word 'Intermediant' should be 'Intermediate'.
  2. [Section II-B-2-b] The decoder name appears inconsistently as 'All-MLP' and 'ALL-MLP'; please unify the terminology.
  3. [Section II-B-2-c] The phrase 'weight Mean Square Error' should be 'weighted Mean Square Error'.
  4. [Section III-A] The number '1, 849' contains an odd space; write '1,849'.
  5. [Table I] The table header 'Constrast' should be 'Contrast'.
  6. [Section III-C-1] The baselines MID and iDiff-IL are compared with BC, but the manuscript does not describe how these models were adapted to the ESD task or what hyperparameters were used; a brief implementation note would improve reproducibility.
  7. [Figures 5(b) and 6] The predicted and ground-truth confidence maps are shown with color gradients but no colorbar or value labels, making it difficult for the reader to judge quantitative agreement; adding colorbars would improve interpretability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the confidence-map prediction targets are fixed by expert annotations and a deterministic distance-ratio algorithm, and RCMNet is evaluated against held-out labels without reusing the label-generation rule.

full rationale

The derivation chain is: DREAMS videos are recorded with an external robotic system (cited from prior work), expert endoscopists annotate dissection trajectories and safety margins, the paper's angular-difference algorithm converts those annotations into confidence maps, and RCMNet is trained to regress those fixed maps and is evaluated on held-out clips. No equation reduces the prediction to the label-generation rule: RCMNet's decoder does not take the trajectory or margin annotations as input at inference, and the angular nearest-margin selection is not re-run by the network. The reported MAE of 3.18 is therefore a genuine measure of how well the network reproduces the self-defined target, not a fitted parameter renamed as a prediction. The trajectory-suggestion evaluation (BC vs MID vs iDiff-IL) is likewise supervised against expert-annotated coordinates. References [9] and [10] are self-citations to the robotic platform used for data collection, but they are not invoked as a uniqueness theorem nor to forbid alternatives, and the dataset/code are released for external checking. The ambiguity in Eq. (1) -- confidence unclamped and undefined on the trajectory -- is a reproducibility and target-definition concern, not a circularity: it does not make the prediction equivalent to its input by construction.

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

The central result depends on three hand-set parameters and two domain assumptions. The confidence formula in Eq. (1) requires an unstated clipping step to be a [0,1] map, and the expert annotations are treated as ground truth without clinical outcome validation.

free parameters (3)
  • Distance threshold for curved dissection areas = Not reported
    Introduced in Section II-A to prevent confidence exceeding 1 when the dissection area is curved; value chosen by hand and never specified.
  • 0.4 mm trajectory offset = 0.4 mm
    Used to adjust the optimal trajectory for knife-tip diameter; estimated by endoscopists during labeling, Section II-A.
  • Outside-margin loss weight = 10
    Set in the weighted MSE loss (Eq. 2) to penalize nonzero confidence outside the safety margin; hand-chosen.
assumptions (2)
  • domain assumption The expert-annotated trajectory and safety margin, converted by the angular-difference algorithm, constitute the ground truth for optimal dissection behavior.
    This assumption underpins dataset construction and evaluation; it is stated in Section II-A but never validated against clinical outcomes.
  • ad hoc to paper The confidence ratio in Eq. (1), after some unstated clamping or normalization, yields a valid [0,1] safety map.
    The paper does not state how values above 1 are handled, yet the task requires bounded confidence.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal Dissection." pith.science (2026). https://pith.science/paper/VOB5ND7N

@misc{pith2026241118884,
  author       = {Pith},
  title        = {Pith review of: ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal Dissection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VOB5ND7N}},
  note         = {Machine review of arXiv:2411.18884}
}
abstract

Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy. Accurate prediction of dissection trajectories is crucial for better decision-making, reducing intraoperative errors, and improving surgical training. Nevertheless, predicting these trajectories is challenging due to variable tumor margins and dynamic visual conditions. To address this issue, we create the ESD Trajectory and Confidence Map-based Safety Margin (ETSM) dataset with $1849$ short clips, focusing on submucosal dissection with a dual-arm robotic system. We also introduce a framework that combines optimal dissection trajectory prediction with a confidence map-based safety margin, providing a more secure and intelligent decision-making tool to minimize surgical risks for ESD procedures. Additionally, we propose the Regression-based Confidence Map Prediction Network (RCMNet), which utilizes a regression approach to predict confidence maps for dissection areas, thereby delineating various levels of safety margins. We evaluate our RCMNet using three distinct experimental setups: in-domain evaluation, robustness assessment, and out-of-domain evaluation. Experimental results show that our approach excels in the confidence map-based safety margin prediction task, achieving a mean absolute error (MAE) of only $3.18$. To the best of our knowledge, this is the first study to apply a regression approach for visual guidance concerning delineating varying safety levels of dissection areas. Our approach bridges gaps in current research by improving prediction accuracy and enhancing the safety of the dissection process, showing great clinical significance in practice.

Figures

Figures reproduced from arXiv: 2411.18884 by the authors.

Figure 1
Figure 1. Overview of our workflow. (a) The dual-arm robotic-based ESD vs. Conventional ESD: The dissection trajectory in the limited endoscopic view during conventional ESD is partial. Dissection trajectory for robot-assisted ESD is more complete. (b) ETSM Dataset Construction: The dataset preprocessing involves video downsampling, frame extraction, and removal of black margins. Data annotation includes marking dissection tr… view at source ↗
Figure 2
Figure 2. Case illustrations of confidence generation. (a) The point in the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview of our RCMNet. The image encoder, leveraging [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Our ETSM dataset visualization. Frames are selected from videos of submucosal dissection tasks performed using a dual-arm robotic system. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Results visualization. (a) Dissection trajectory suggestion results. Green lines represent the ground truth dissection trajectories, while red lines [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 7
Figure 7. Figure 7: Out of domain evaluation. We assess our RCMNet on two additional [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BleedOrigin: Dynamic Bleeding Source Localization in Endoscopic Submucosal Dissection via Dual-Stage Detection and Tracking

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A new ESD bleeding-source dataset and a dual-stage detection-tracking framework report 96.85% onset, 70.24% source, and 96.11% tracking accuracy within defined tolerances.

Reference graph

Works this paper leans on

28 extracted references · 19 canonical work pages · cited by 1 Pith paper

  1. [1]

    Endoscopic submucosal dissection (esd) compared with gastrectomy for treatment of early gastric neoplasia: a retrospective cohort study,

    P. W. Y . Chiu, A. Y . B. Teoh, K. F. To, S. K. H. Wong, S. Y . W. Liu, C. C. Lam, M. Y . Yung, F. K. L. Chan, J. Y . W. Lau, and E. K. W. Ng, “Endoscopic submucosal dissection (esd) compared with gastrectomy for treatment of early gastric neoplasia: a retrospective cohort study,” Surgical endoscopy, vol. 26, pp. 3584–3591, 2012

    work page 2012

  2. [2]

    Symmetric dilated convolution for surgical gesture recognition,

    J. Zhang, Y . Nie, Y . Lyu, H. Li, J. Chang, X. Yang, and J. J. Zhang, “Symmetric dilated convolution for surgical gesture recognition,” in Medical Image Computing and Computer Assisted Intervention– MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23 . Springer, 2020, pp. 409–418

    work page 2020

  3. [3]

    Factors predictive of perforation during endoscopic submucosal dis- section for the treatment of colorectal tumors,

    E. Kim, K. Cho, K. Park, K. Lee, B. Jang, W. Chung, and J. Hwang, “Factors predictive of perforation during endoscopic submucosal dis- section for the treatment of colorectal tumors,” Endoscopy, vol. 43, no. 07, pp. 573–578, 2011

    work page 2011

  4. [4]

    A novel trajectory predicting method of catheter for the vascular interventional surgical robot,

    J. Guo, Y . Sun, and S. Guo, “A novel trajectory predicting method of catheter for the vascular interventional surgical robot,” in 2020 IEEE International Conference on Mechatronics and Automation (ICMA) . IEEE, 2020, pp. 1304–1309

    work page 2020

  5. [5]

    davincinet: Joint prediction of motion and surgical state in robot- assisted surgery,

    Y . Qin, S. Feyzabadi, M. Allan, J. W. Burdick, and M. Azizian, “davincinet: Joint prediction of motion and surgical state in robot- assisted surgery,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 2921–2928

    work page 2020

  6. [6]

    Real-time trajectory predic- tion of laparoscopic instrument tip based on long short-term memory neural network in laparoscopic surgery training,

    Z. Wang, Z. Yan, Y . Xing, and H. Wang, “Real-time trajectory predic- tion of laparoscopic instrument tip based on long short-term memory neural network in laparoscopic surgery training,” The International Journal of Medical Robotics and Computer Assisted Surgery , vol. 18, no. 6, p. e2441, 2022

    work page 2022

  7. [7]

    Imitation learning from expert video data for dissection trajectory prediction in endoscopic surgical procedure,

    J. Li, Y . Jin, Y . Chen, H.-C. Yip, M. Scheppach, P. W.-Y . Chiu, Y . Yam, H. M.-L. Meng, and Q. Dou, “Imitation learning from expert video data for dissection trajectory prediction in endoscopic surgical procedure,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2023, pp. 494–504

    work page 2023

  8. [8]

    Stochastic trajectory prediction via motion indeterminacy diffusion,

    T. Gu, G. Chen, J. Li, C. Lin, Y . Rao, J. Zhou, and J. Lu, “Stochastic trajectory prediction via motion indeterminacy diffusion,” in Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 17 113–17 122

    work page 2022

Show all 28 references
  1. [9]

    Transendoscopic flexible parallel continuum robotic mechanism for bimanual endoscopic submucosal dissection,

    H. Gao, X. Yang, X. Xiao, X. Zhu, T. Zhang, C. Hou, H. Liu, M. Q.- H. Meng, L. Sun, X. Zuo, et al. , “Transendoscopic flexible parallel continuum robotic mechanism for bimanual endoscopic submucosal dissection,” The International Journal of Robotics Research , vol. 43, no. 3, ...

    2024

  2. [10]

    Novel miniature transendoscopic telerobotic system for endoscopic submucosal dissection (with videos),

    X. Yang, H. Gao, S. Fu, R. Ji, C. Hou, H. Liu, N. Luan, H. Ren, L. Sun, J. Yang, Z. Zhou, X. Yang, L. Sun, Y . Li, and X. Zuo, “Novel miniature transendoscopic telerobotic system for endoscopic submucosal dissection (with videos),” Gastrointestinal Endoscopy , vol. 99, no. 2, ...

    2024

  3. [11]

    Exploring the limitations of behavior cloning for autonomous driving,

    F. Codevilla, E. Santana, A. M. L ´opez, and A. Gaidon, “Exploring the limitations of behavior cloning for autonomous driving,” in Proceed- ings of the IEEE/CVF international conference on computer vision , 2019, pp. 9329–9338

    2019

  4. [12]

    Dinov2: Learning robust visual features without supervision,

    M. Oquab, T. Darcet, T. Moutakanni, H. V o, M. Szafraniec, V . Khali- dov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, et al., “Dinov2: Learning robust visual features without supervision,” arXiv preprint arXiv:2304.07193, 2023

    2023 arXiv

  5. [14]

    Segformer: Simple and efficient design for semantic segmen- tation with transformers,

    E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmen- tation with transformers,” in Neural Information Processing Systems (NeurIPS), 2021

    2021

  6. [15]

    LoRA: Low-rank adaptation of large language models,

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, and W. Chen, “LoRA: Low-rank adaptation of large language models,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=nZeVKeeFYf9

    2022

  7. [16]

    Adam: A method for stochastic optimization,

    D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR) , San Diega, CA, USA, 2015

    2015

  8. [17]

    Social- stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,

    A. Mohamed, K. Qian, M. Elhoseiny, and C. Claudel, “Social- stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 14 424–14 432

    2020

  9. [18]

    Recursive social behavior graph for trajectory prediction,

    J. Sun, Q. Jiang, and C. Lu, “Recursive social behavior graph for trajectory prediction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 660–669

    2020

  10. [19]

    Cross-scene crowd counting via deep convolutional neural networks,

    C. Zhang, H. Li, X. Wang, and X. Yang, “Cross-scene crowd counting via deep convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 833– 841

    2015

  11. [20]

    The coefficient of deter- mination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation,

    D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of deter- mination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation,” Peerj computer science , vol. 7, p. e623, 2021

    2021

  12. [21]

    Cornet: Context-based ordinal regression network for monocular depth estimation,

    X. Meng, C. Fan, Y . Ming, and H. Yu, “Cornet: Context-based ordinal regression network for monocular depth estimation,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 7, pp. 4841–4853, 2021

    2021

  13. [22]

    Multidimensional measure matching for crowd counting,

    H. Lin, X. Hong, Z. Ma, Y . Wang, and D. Meng, “Multidimensional measure matching for crowd counting,” IEEE Transactions on Neural Networks and Learning Systems , 2024

    2024

  14. [23]

    Boosting crowd counting via multifaceted attention,

    H. Lin, Z. Ma, R. Ji, Y . Wang, and X. Hong, “Boosting crowd counting via multifaceted attention,” in CVPR, 2022

    2022

  15. [24]

    To choose or to fuse? scale selection for crowd counting,

    Q. Song, C. Wang, Y . Wang, Y . Tai, C. Wang, J. Li, J. Wu, and J. Ma, “To choose or to fuse? scale selection for crowd counting,” The Thirty- Fifth AAAI Conference on Artificial Intelligence (AAAI-21) , 2021

    2021

  16. [25]

    Benchmarking neural network ro- bustness to common corruptions and perturbations,

    D. Hendrycks and T. Dietterich, “Benchmarking neural network ro- bustness to common corruptions and perturbations,” arXiv preprint arXiv:1903.12261, 2019

    1903 arXiv

  17. [26]

    Segmenter: Trans- former for semantic segmentation,

    R. Strudel, R. Garcia, I. Laptev, and C. Schmid, “Segmenter: Trans- former for semantic segmentation,” arXiv preprint arXiv:2105.05633 , 2021

    2021 arXiv

  18. [27]

    Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes,

    Y . Hong, H. Pan, W. Sun, and Y . Jia, “Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes,” arXiv preprint arXiv:2101.06085 , 2021

    2021 arXiv

  19. [28]

    Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,

    S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y . Wang, Y . Fu, J. Feng, T. Xiang, P. H. Torr, and L. Zhang, “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in CVPR, 2021

    2021

  20. [2014]

    Available: http://arxiv.org/abs/1411.4038

    [Online]. Available: http://arxiv.org/abs/1411.4038

    arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.