Decision-based AI Visual Navigation for Cardiac Ultrasounds

Andy Dimnaku; Arun Padmanabhan; Dominic Yurk; Mandar Aras; Yaser Abu-Mostafa; Zhiyuan Gao

REVIEW 4 major objections 6 minor 11 references

Decision-based AI Visual Navigation for Cardiac Ultrasounds

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read A video classifier trained only on presence labels can point to the inferior vena cava in real time.

desk verdict A real-time IVC guidance system with a clever zero-shot transfer, but the headline localization numbers are conditional on the detector already finding the IVC, and the novelty claim ignores CAM-style methods. read the letter →

arxiv 2504.12535 v1 pith:H3QDCKN5 submitted 2025-04-16 cs.CV cs.AI

Andy Dimnaku , Dominic Yurk , Zhiyuan Gao , Arun Padmanabhan , Mandar Aras , Yaser Abu-Mostafa This is my paper · ORCID

classification cs.CVcs.AI

keywords AIguidancedeeplearningcardiacultrasoundinferiorvenacavazero-shotlocalizationfeature-mapvideoclassificationreal-timenavigation

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 claims that a video classifier trained only to decide whether the inferior vena cava is present can also act as a real-time visual guide: internal feature maps from a late network layer, normalized and interpolated back to the input image, mark the vessel's location without any spatial supervision. This turns a binary label—IVC present or not—into an on-screen cursor that tells an inexperienced operator where to look, and it works zero-shot on a different ultrasound device and on a model trained for a different, more abstract task. The consequence is that ultrasound guidance no longer needs annotated bounding boxes or explicit spatial losses, which lowers the barrier to AI-assisted cardiac imaging in clinics and on portable scanners. The authors report that the guidance system is undergoing clinical trials and is available on a handheld-device app.

What carries the argument

The central object is Algorithm 1, a lightweight localization step appended to a trained video classifier. It uses the feature map after the third ResNet block of the X3D network, the last layer that preserves both spatial and temporal information, normalizes the channel dimension, interpolates to original pixel space, selects the highest-valued pixels, filters out static black pixels and outliers farther than 40 pixels from the mean, and plots the mean point as a 20-pixel-radius circle. This feature map carries the same signal the paper verifies offline with an explanation method: the input pieces around the IVC are the ones that most change the decision when blacked out. Because the localization step is a few tensor operations rather than a costly per-pixel attribution run, it can execute in real time during scanning.

What would settle it

Run the full decision-plus-localization pipeline on an unselected set of ultrasound sweeps known to contain the IVC, including sweeps the decision model classifies as negative, and count how often the green circle actually lands on the IVC; if the missed detections are numerous, the conditional 97% and 99.67% figures would overstate real-world performance.

Watch

Extended reading notes

Core claim

The central discovery is that the feature maps of a decision model encode spatial location: the region with the largest normalized activation values after the third ResNet block of an X3D video classifier coincides with the IVC, and averaging those high-activation pixels produces a stable landmark. The localization algorithm runs only when the decision model's binary output is positive, then normalizes the feature map's channels, interpolates the tensor back to input resolution with a 3D spline, retains the highest-valued pixels, removes black pixels and outliers more than 40 pixels from the mean, and draws a 20-pixel-radius green circle at the mean location. On 693 hospital videos for which the decision model detected an IVC, it located the vessel in 97% of cases; zero-shot on 916 videos from a handheld low-cost device it succeeded in 99.67%; and zero-shot on a different model trained to estimate right atrial pressure from sniff videos, with no IVC-detection objective, it located the IVC in all 30 test cases.

Load-bearing premise

The reported localization accuracies are measured only on videos for which the decision model already said an IVC was present, so the numbers describe how well the system points when it detects, not how often it finds the IVC in the first place.

Editorial extensions

If this is right

IVC guidance can be delivered as an on-screen landmark by a model trained only on video-level presence labels; no bounding-box annotations or spatial loss functions are required.
The same decision-plus-localization design should transfer to other anatomical targets, since any binary video classifier whose decision depends on a spatially localized structure can be turned into a visual guide.
Because the localization step does not alter the decision model, the guidance system can be added to an already validated classifier without retraining.
Zero-shot results on a different device and a different architecture indicate that the localization signal is not tied to a single model or image quality, so portability to lower-cost scanners is plausible.

Reading between the lines

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

The paper does not test this, but if the localization signal is a general property of classifiers with localized targets, the same recipe could convert classifiers for other one-location findings—such as gallbladder, fetal heartbeat, or lung consolidation—into real-time finders without detection training.
A discriminating experiment would separate two readings: the algorithm may be locating the anatomical object, or it may simply be highlighting whatever image region the classifier found most decision-relevant. Running Algorithm 1 on a classifier trained for a non-anatomical localized target would tell the difference.
Because localization is gated on a positive decision, the pipeline is a confirm-and-point system rather than a search system; the paper's own 62% clinical-trial accuracy for decision-only navigation suggests the harder part is finding the IVC, not drawing the circle once it is found.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Two things to know. First, this is a real, deployed system: a video classifier (X3D) decides if the IVC is present, and a lightweight post-hoc saliency map on the third ResNet block draws a circle on the IVC in real time. Second, the reported 97% and 99.67% localization accuracies only count videos where the classifier already said 'IVC present.' If the classifier misses the IVC, the system stays silent, and the paper never tells you how often that happens on the test sets. The 62% clinical-trial number for the decision-only system suggests the gap could be large.

What's genuinely good: the zero-shot transfer to the RAP classifier (SlowFast) and to the Butterfly iQ device is a meaningful robustness check. A 100% localization rate on 30 sniff studies is not a proof of anything, but it shows the feature-map signal transfers across architecture and task. The algorithm is simple enough to run in real time, and the paper is clearly written about the deployment logic.

The soft spots are the conditional evaluation, the missing baselines, and the novelty claim. No comparison to CAM or Grad-CAM, despite the method being essentially a channel-normalized, thresholded feature map. The claim to be the first to use learned feature maps for real-time guidance is not supported by the cited literature. The localization thresholds were tuned on 90 videos, and the manual-review protocol is described only briefly. None of these kill the paper, but they mean the headline numbers overstate what a novice will experience.

Who is this for? A reader working on ultrasound guidance or interpretability for video classifiers will find the system description and transfer results useful. A methods researcher will find little that is new beyond a specific application.

Recommendation: send it to peer review, but expect the reviewers to demand end-to-end accuracy numbers, a sensitivity analysis, and an honest positioning against CAM-style methods. If the authors can provide those, this could be a solid application paper.

Referee Report

4 major / 6 minor

Summary. The paper describes a real-time IVC localization system built on top of a binary video classifier. The authors train an X3D model to detect IVC presence in transthoracic echocardiogram videos, then apply a localization algorithm that extracts the feature map after the third ResNet block, normalizes it over the channel dimension, interpolates it to input resolution, keeps the n highest-valued pixels after removing black pixels and pixels farther than 40 pixels from the mean, and draws a 20-pixel green circle at the mean position. Localization is gated on a positive decision from the classifier. The authors report 97% localization accuracy on 693 hospital videos where the decision model detected an IVC, 99.67% on 916 Butterfly iQ videos with positive decisions, and 100% on 30 sniff videos from a SlowFast RAP classifier without retraining. They frame the contribution as object-detection-like localization without spatial supervision, and they mention a clinical trial of the decision-only navigation system at 62% accuracy.

Significance. If the results held as stated, the paper would demonstrate a practical and inexpensive route to spatially localizing an anatomical structure from a video-level classifier, with possible value for novice ultrasound operators and low-cost devices. The computational efficiency of the feature-map localization is a genuine strength, as is the attempt to test on an out-of-distribution device (Butterfly iQ) and on a different architecture and task (SlowFast RAP). The SHAP analysis is a useful sanity check that the decision model attends to the IVC. The claims are falsifiable and clinically relevant, but the manuscript does not provide code or data, and the evaluation has a conditional-filtering problem, lacks baselines, and rests on a small transfer set. The significance is therefore currently prospective rather than fully established.

major comments (4)

[Section 3.2 and Algorithm 1] The headline localization rates are conditional on the decision model's positive output. Algorithm 1 explicitly does not localize when the predicted probability is not positive, and the 693-video hospital set and 916-video Butterfly set are both described as videos 'where the decision model detected an IVC.' Thus the 97% and 99.67% numbers estimate P(correct localization | positive decision), not the probability that the navigation system finds the IVC in a real scan. The latter is P(true positive decision) × P(correct localization | positive decision), and the decision model's sensitivity is never reported. If sensitivity were 85%, a 97% conditional rate would become roughly 82% end-to-end; lower sensitivity increases the gap. Because the test sets are defined by model predictions rather than by ground-truth labels, it is also unclear whether false positives (videos with no IVC) are included. Please report the full confusion matrix on complete, ground-truth-labeled test sets and make end-to-end success the primary navigation metric.
[Sections 3.2-3.3] No comparison to any baseline localization method is provided. The evaluation only asks a manual reviewer whether the 20-pixel circle contains the IVC, and positive clips are typically acquired with the IVC near the center of the image. To establish that Algorithm 1 contributes spatial information, the authors should compare against a center-of-image baseline, a random-patch control, a standard attribution map such as CAM or Grad-CAM thresholded the same way, and ideally a supervised object detector. Reporting distance between the predicted point and the IVC center, along with precision/recall for containment, would be more informative than a single binary accuracy.
[Section 2.4 and Algorithm 1] The localization algorithm has several free parameters - the feature layer (3rd ResNet block), the number of retained pixels n, the 40-pixel outlier threshold, and the 20-pixel annotation radius - and Section 2.4 says these were 'adjusted' on a set of 90 hospital-grade IVC scans. The paper does not report how the tuning set was used, whether it is disjoint from the 693-video test set, or how sensitive the results are to each parameter. Without this information, the hospital result cannot be considered a fully out-of-sample estimate, and the phrase 'zero-shot on a RAP classification model' is stronger than what is shown because the algorithm's hyperparameters and layer choice were carried over from the IVC model rather than being intrinsic to the method. Please report the tuning protocol, dataset overlap, and a sensitivity analysis.
[Section 3.3] The transfer result rests on 30 patients from a single hospital machine, with 10 patients per RAP group. A 100% success rate over 30 independent trials has a Clopper-Pearson 95% lower bound of roughly 88%, so the result should be reported with a confidence interval. In addition, all 30 videos are sniff scans, so the IVC may be consistently framed; the authors should state the inclusion criteria and, to support the zero-shot generalization claim, test on at least one additional device or viewing protocol.

minor comments (6)

[Section 2.3] Algorithm 1 uses inconsistent notation: the input is (1, t, h, w) but the interpolation target is (1, tx, hx, wx), and it is not stated whether the 40-pixel and 20-pixel quantities are in original pixels or feature-map pixels.
[Section 2.2] Figure 2's caption and Section 2.2 refer to a 'level bar' and to 'blackout' combinations without defining them; please add a color scale and explain how the SHAP values are normalized.
[Section 3.1] In Section 3.1, '62% ± 9.98 accuracy' should specify whether this is a 95% confidence interval and over how many patients or trials; also clarify how the comparison to commercial systems [5,6] was performed.
[References] References [6] and [8] are the same paper and should be merged.
[Section 3.3] The text in Section 3.3 states the system 'has not been tested for guidance of cardiac views other than the IVC,' which is an appropriate caveat; the title and abstract should avoid implying a general-purpose visual navigation method beyond this single structure.
[Section 4] The deployed system is said to be 'available on the Butterfly iQ app,' but the manuscript provides no version, link, or validation of the deployed implementation; clarify whether the evaluated model is exactly the deployed one.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: localization is evaluated against external manual review labels; the conditional test-set filtering is an evaluation-scope limitation, not a derivation loop.

full rationale

The derivation chain is self-contained. The X3D decision model is trained on binary IVC-present labels, and Algorithm 1 post-hoc selects high-magnitude pixels from the third ResNet feature map; the localization output is scored by manual review of whether a 20-pixel circle contains the IVC, which is an external ground truth independent of the model's own prediction. No equation defines the localization output in terms of the reported accuracy, and no hyperparameter is fitted to the test statistic. The RAP transfer experiment uses a SlowFast model from the authors' prior work (ref. 4), but this is not load-bearing: the transfer claim is tested directly on 30 patients with manual review and is not derived from the cited paper. The conditional filtering in Sec. 3.2 (693 and 916 videos 'where the decision model detected an IVC') means localization rates are P(correct | positive decision), but this is an evaluation-scope limitation rather than a circular derivation, since Algorithm 1 explicitly states it does not localize when the decision is negative. Therefore no circular step is present.

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

No new physical or model entities are introduced. The method relies on standard deep learning assumptions, a small set of hand-tuned localization parameters, and the representativeness of a small tuning set, all of which are inadequately validated in the paper.

free parameters (4)

n (number of largest pixels retained) = not specified
Used to select candidate pixels in Algorithm 1; adjusted on a set of 90 hospital-grade IVC scans (Section 2.4).
spatial distance threshold = 40 pixels
Pixels more than 40 pixels from the mean are removed; chosen by hand during development (Algorithm 1).
annotation circle radius = 20 pixels
The green circle radius used to display and evaluate localization; fixed by the authors (Algorithm 1).
feature layer choice = 3rd ResNet block
Layer for localization was selected by testing layers, with the 3rd block chosen as the last with spatial-temporal info (Section 2.3).

assumptions (5)

domain assumption X3D and SlowFast networks trained on these video tasks generalize to internal test sets and to the Butterfly domain.
The entire evaluation assumes the trained models work as classifiers; no distribution analysis is provided.
domain assumption SHAP Monte Carlo estimates with 1 million iterations accurately identify the features the model uses.
Used in Section 2.2 to justify that the model looks at the IVC; no convergence diagnostics are shown.
domain assumption The feature map after the 3rd ResNet block retains enough spatial information for localization.
Central to Algorithm 1; validated only by the tuning set, not by comparison to other layers.
domain assumption Manual reviewer annotations of whether the green circle contains the IVC are correct.
Localization accuracy is determined by a manual reviewer (Section 3.2); no inter-rater reliability is reported.
domain assumption The 90-video adjustment set is representative of the high-quality and Butterfly test sets.
Thresholds tuned on this set are then applied zero-shot; potential domain shift is not quantified.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Decision-based AI Visual Navigation for Cardiac Ultrasounds." pith.science (2026). https://pith.science/paper/H3QDCKN5

@misc{pith2026250412535,
  author       = {Pith},
  title        = {Pith review of: Decision-based AI Visual Navigation for Cardiac Ultrasounds},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H3QDCKN5}},
  note         = {Machine review of arXiv:2504.12535}
}

read the original abstract

Ultrasound imaging of the heart (echocardiography) is widely used to diagnose cardiac diseases. However, obtaining an echocardiogram requires an expert sonographer and a high-quality ultrasound imaging device, which are generally only available in hospitals. Recently, AI-based navigation models and algorithms have been used to aid novice sonographers in acquiring the standardized cardiac views necessary to visualize potential disease pathologies. These navigation systems typically rely on directional guidance to predict the necessary rotation of the ultrasound probe. This paper demonstrates a novel AI navigation system that builds on a decision model for identifying the inferior vena cava (IVC) of the heart. The decision model is trained offline using cardiac ultrasound videos and employs binary classification to determine whether the IVC is present in a given ultrasound video. The underlying model integrates a novel localization algorithm that leverages the learned feature representations to annotate the spatial location of the IVC in real-time. Our model demonstrates strong localization performance on traditional high-quality hospital ultrasound videos, as well as impressive zero-shot performance on lower-quality ultrasound videos from a more affordable Butterfly iQ handheld ultrasound machine. This capability facilitates the expansion of ultrasound diagnostics beyond hospital settings. Currently, the guidance system is undergoing clinical trials and is available on the Butterfly iQ app.

Figures

Figures reproduced from arXiv: 2504.12535 by the authors.

**Figure 1.** Figure of the X3D and SlowFast video decision model architectures. X3D Our base model for IVC classification utilizes an X3D architecture [9]. The X3D architecture has high spatiotemporal understanding and is a lighter model with regard to network width and parameters. The network’s lighter aspects allow for easier conversion to real-time applications. Specifically, our model is made up of 4 expanded ResNet blocks,… view at source ↗

**Figure 2.** One frame of a 1 million iteration SHAP run of an IVC video. The SHAP values can be displayed in the below level bar signifying the relative importance. Darker color represents a larger magnitude. In order to explain what input features our decision model was using to identify the presence or absence of the IVC we used an iterative SHAP process. We split the input IVC video into 16 pieces per frame for the 32 frames… view at source ↗

**Figure 3.** Samples of the navigation algorithm run on real IVC videos. The top and bottom rows are from the high quality hospital and more affortable Butterfly iQ ultrasounds respectively. The green dot is the localized location of the IVC based on Algorithm 1. Butterfly iQ The navigation system was run zero-shot on a set of low-quality IVC scans. The scans were taken from the clinical trial previously mentioned. The dataset … view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: 3 examples of Algorithm 1 run on the RAP classification model. (a), (b), and (c) are example videos at low, medium and high RAPs respectively. The images are single frames of the video for the sake of display. algorithm was run on a test set of 30 patients with 10 of l…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

11 extracted references · 8 canonical work pages

[1]

Journal of Cardiac Failure, Volume 29, Issue 10, 1412 - 1451

HeartFailureEpidemiologyandOutcomesStatistics:AReportoftheHeartFailure Society of America Bozkurt, Biykem et al. Journal of Cardiac Failure, Volume 29, Issue 10, 1412 - 1451

work page
[2]

Diagnosis of fluid overload: from conventional to contempo- rary concepts

Koratala A, Ronco C, Kazory A. Diagnosis of fluid overload: from conventional to contempo- rary concepts. Cardiorenal Med. 2022;12:141–154

work page 2022
[3]

H., & Felker, G

Ellison, D. H., & Felker, G. M. (2017). Diuretic Treatment in Heart Failure. New England Journal of Medicine, 377(20), 1964–1975. doi:10.1056/NEJMra1703100

work page doi:10.1056/nejmra1703100 2017
[4]

P., Labrecque Langlais, E., Avram, R., Aras, M

Yurk, D., Barrios, J. P., Labrecque Langlais, E., Avram, R., Aras, M. A., Abu- Mostafa, Y., ... Tison, G. H. (2024). Automated Assessment of Right Atrial Pres- sure From Ultrasound Videos Using Machine Learning. JACC: Advances, 3(9, Part 2), 101192. doi:10.1016/j.jacadv.2024.101192

arXiv 2024
[5]

I.,Moreno, M.,Ignatowski, D.,

Mor-Avi, V.,Khandheria, B.,Klempfner, R.,Cotella,J. I.,Moreno, M.,Ignatowski, D., ... Lang, R. M. (2023). Real-Time Artificial Intelligence–Based Guidance of Echocardiographic Imaging by Novices: Image Quality and Suitability for Diagnos- tic Interpretation and Quantitative Analysis. Circulation: Cardiovascular Imaging, 16(11), e015569. doi:10.1161/CIRCIM...

work page doi:10.1161/circimaging.123.015569 2023
[7]

Bao, M., Wang, Y., Wei, X., Jia, B., Fan, X., Lu, D., ... Zhu, H. (2024, October). Real-world Visual Navigation for Cardiac Ultrasound View Planning. Proceedings of Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, LNCS 15001. Springer Nature Switzerland

work page 2024
[8]

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use

Narang A, Bae R, Hong H, et al. Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use. JAMA Cardiol. 2021;6(6):624–632. doi:10.1001/jamacardio.2021.0185

arXiv 2021
[9]

(2020, June)

Feichtenhofer, C. (2020, June). X3D: Expanding Architectures for Efficient Video Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

work page 2020

Show all 11 references

[10]

(2019, October)

Feichtenhofer, C., Fan, H., Malik, J., & He, K. (2019, October). SlowFast Networks for Video Recognition. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

2019
[11]

M., & Lee, S.-I

Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 30)

2017
[12]

Deep Residual Learning for Image Recog- nition,

K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recog- nition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90

2016 doi

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

Journal of Cardiac Failure, Volume 29, Issue 10, 1412 - 1451

HeartFailureEpidemiologyandOutcomesStatistics:AReportoftheHeartFailure Society of America Bozkurt, Biykem et al. Journal of Cardiac Failure, Volume 29, Issue 10, 1412 - 1451

work page

[2] [2]

Diagnosis of fluid overload: from conventional to contempo- rary concepts

Koratala A, Ronco C, Kazory A. Diagnosis of fluid overload: from conventional to contempo- rary concepts. Cardiorenal Med. 2022;12:141–154

work page 2022

[3] [3]

H., & Felker, G

Ellison, D. H., & Felker, G. M. (2017). Diuretic Treatment in Heart Failure. New England Journal of Medicine, 377(20), 1964–1975. doi:10.1056/NEJMra1703100

work page doi:10.1056/nejmra1703100 2017

[4] [4]

P., Labrecque Langlais, E., Avram, R., Aras, M

Yurk, D., Barrios, J. P., Labrecque Langlais, E., Avram, R., Aras, M. A., Abu- Mostafa, Y., ... Tison, G. H. (2024). Automated Assessment of Right Atrial Pres- sure From Ultrasound Videos Using Machine Learning. JACC: Advances, 3(9, Part 2), 101192. doi:10.1016/j.jacadv.2024.101192

arXiv 2024

[5] [5]

I.,Moreno, M.,Ignatowski, D.,

Mor-Avi, V.,Khandheria, B.,Klempfner, R.,Cotella,J. I.,Moreno, M.,Ignatowski, D., ... Lang, R. M. (2023). Real-Time Artificial Intelligence–Based Guidance of Echocardiographic Imaging by Novices: Image Quality and Suitability for Diagnos- tic Interpretation and Quantitative Analysis. Circulation: Cardiovascular Imaging, 16(11), e015569. doi:10.1161/CIRCIM...

work page doi:10.1161/circimaging.123.015569 2023

[6] [7]

Bao, M., Wang, Y., Wei, X., Jia, B., Fan, X., Lu, D., ... Zhu, H. (2024, October). Real-world Visual Navigation for Cardiac Ultrasound View Planning. Proceedings of Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, LNCS 15001. Springer Nature Switzerland

work page 2024

[7] [8]

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use

Narang A, Bae R, Hong H, et al. Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use. JAMA Cardiol. 2021;6(6):624–632. doi:10.1001/jamacardio.2021.0185

arXiv 2021

[8] [9]

(2020, June)

Feichtenhofer, C. (2020, June). X3D: Expanding Architectures for Efficient Video Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

work page 2020