arxiv: 2605.02558 · v2 · submitted 2026-05-04 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification

Tzu-Yu Liu , Duan-Shin Lee

Authors on Pith no claims yet

Pith reviewed 2026-05-08 18:14 UTC · model grok-4.3

classification 💻 cs.CV

keywords badminton stroke classificationtemporal context fusionbidirectional modelingtwo-stage trainingaction recognitionsports video analysisadjacent stroke fusion

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The pith

Bidirectional fusion of adjacent stroke context improves badminton stroke classification accuracy.

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

The paper introduces TemPose-TF-ASF as a context-aware extension that incorporates stroke-type information from both preceding and subsequent strokes to address limitations in modeling rich temporal context. It employs a two-stage training and inference strategy in which preliminary predictions from the baseline serve as estimated temporal context to jointly optimize the fusion module and classifier. This explicit bidirectional dependency modeling allows seamless integration into existing state-of-the-art models. A sympathetic reader would care because more accurate fine-grained stroke recognition supports detailed sports analysis and tactical decision support in matches. Experiments confirm consistent gains in Accuracy and Macro-F1, plus notable improvements when the fusion is added to other advanced methods.

Core claim

By reusing preliminary baseline predictions as estimated temporal context, the two-stage strategy guides joint optimization of the Adjacent-Stroke Fusion module and classifier, enabling explicit modeling of bidirectional stroke-type dependencies that yields higher Accuracy and Macro-F1 scores on large-scale badminton datasets while remaining integrable into other state-of-the-art models for further performance gains.

What carries the argument

The Adjacent-Stroke Fusion (ASF) module, which fuses stroke-type information from both preceding and subsequent strokes using preliminary predictions to guide two-stage optimization of bidirectional temporal dependencies.

If this is right

Accuracy and Macro-F1 scores improve consistently over the baseline and its variants on large-scale badminton match data.
The ASF module integrates into other advanced methods and produces notable performance gains in those models as well.
Explicit bidirectional temporal dependency modeling enhances stroke recognition without requiring changes to the core architecture of host models.
The approach exhibits strong transferability and generalization capability across different backbone methods.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The two-stage reuse of preliminary predictions may reduce the annotation burden for full temporal context in training data for sequential action tasks.
Similar bidirectional fusion could apply to other sequential sports actions such as tennis serves or table-tennis strokes where past and future context matters.
If initial prediction quality improves, the same fusion mechanism might support fully end-to-end training rather than staged optimization.
Live match analysis could benefit from the method once error propagation from early predictions is further mitigated.

Load-bearing premise

Preliminary predictions from the baseline model supply sufficiently accurate estimated temporal context to guide joint optimization without introducing harmful error propagation.

What would settle it

Retraining the fusion module with ground-truth adjacent-stroke labels in place of preliminary predictions produces no accuracy gain or a performance drop relative to the baseline.

Figures

Figures reproduced from arXiv: 2605.02558 by Duan-Shin Lee, Tzu-Yu Liu.

**Figure 2.** Figure 2: In the Temporal Fusion configuration, player position and shuttlecock view at source ↗

**Figure 1.** Figure 1: Architecture of Stroke Fusion view at source ↗

**Figure 2.** Figure 2: Architecture of TemPose-TF-ASF TemPose-TF-ASF incorporates semantic information from both preceding and subsequent strokes, enabling context-aware refinement of the target stroke prediction view at source ↗

**Figure 3.** Figure 3: TSCR Pipeline of TemPose-TF-ASF For TemPose-TF-ASF, Adjacent-Stroke inputs are initialized as zero vectors in the first stage, reducing the model to the original TemPose-TF [12] for context-free stroke prediction. The predicted stroke labels within each batch are then aggregated and reformulated as estimated Adjacent-Stroke inputs. In the second stage, these estimated labels are fed back to refine stroke p… view at source ↗

**Figure 4.** Figure 4: Architecture of the LSTM Predictor (LP). were retained and split into 30 matches for training, 5 for validation, and 5 for testing, resulting in a total of 33,429 annotated strokes. In addition, two stroke categories with fewer than 50 instances in the entire dataset, namely “push” and “lob”, were merged into a single category labeled “push lob”, while “wrist smash” was merged into the “smash” category. To… view at source ↗

**Figure 5.** Figure 5: Architecture of the Transformer Predictor (TP). that ASF is backbone-agnostic and exhibits strong transferability and generalization. In addition to the full ASF design, several variant models incorporating only partial stroke-type information were evaluated. These variants include TemPose-TF-PSF (Pre-Stroke Fusion), which incorporates only preceding strokes; TemPose-TF-NSF (Next-Stroke Fusion), which … view at source ↗

read the original abstract

Accurate badminton stroke prediction is crucial for fine-grained sports analysis and tactical decision support. However, existing methods struggle to model rich temporal context. This paper introduces TemPose-TF-ASF (Adjacent-Stroke Fusion), a context-aware extension of TemPose. It enhances stroke recognition by incorporating stroke-type information from both preceding and subsequent strokes. A two-stage training and inference strategy is adopted. Preliminary predictions from the baseline model are reused as estimated temporal context. These predictions guide the joint optimization of the ASF module and the classifier. By explicitly modeling bidirectional temporal stroke dependencies, the proposed method can be seamlessly integrated into existing state-of-the-art models. Experiments on a large-scale badminton match dataset show consistent improvements over the baseline and its variants in terms of Accuracy and Macro-F1. Moreover, integrating ASF into other advanced methods yields notable performance gains. These results demonstrate strong transferability and generalization capability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This adds a two-stage bidirectional fusion module on top of TemPose that reuses baseline predictions as adjacent-stroke context, but the abstract supplies no numbers or ablations to show the gains are real rather than artifacts of error propagation.

read the letter

The paper's core move is straightforward: take the TemPose baseline, run a first stage to get preliminary stroke labels, then feed those as estimated preceding and following context into a new Adjacent-Stroke Fusion module that is trained jointly with the classifier. The claim is that this explicit bidirectional modeling improves accuracy and macro F1 on a large badminton dataset and can be dropped into other models with similar gains.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces TemPose-TF-ASF, a context-aware extension of the TemPose baseline for badminton stroke classification. It incorporates bidirectional temporal context via an Adjacent-Stroke Fusion (ASF) module trained in two stages: preliminary baseline predictions are reused as estimated preceding and subsequent stroke labels to guide joint optimization of ASF and the classifier. The authors claim consistent Accuracy and Macro-F1 gains over the baseline and variants on a large-scale badminton dataset, plus transferability when ASF is plugged into other SOTA models.

Significance. If the performance claims are substantiated with quantitative results and controls, the bidirectional fusion approach could meaningfully advance temporal modeling for fine-grained action recognition in sports analytics, where stroke sequences encode tactical dependencies. The two-stage reuse of predictions is a pragmatic way to inject context without requiring fully annotated sequences at inference. No machine-checked proofs or parameter-free derivations are present, but the transferability claim, if verified, would be a practical strength.

major comments (2)

Abstract: the central claim of 'consistent improvements' and 'notable performance gains' is asserted without any numerical values, error bars, dataset splits, ablation tables, or statistical significance tests. This prevents evaluation of whether the reported gains exceed baseline variance or are load-bearing for the two-stage ASF contribution.
Abstract (two-stage strategy description): preliminary baseline predictions are reused as bidirectional context to 'guide the joint optimization' of ASF, yet no oracle ablation (ground-truth adjacent labels vs. predicted labels) or first-stage error-rate analysis is mentioned. If baseline error rates are non-negligible, any observed gains could partly compensate for propagated mistakes rather than demonstrate genuine context modeling; this is load-bearing for the method's validity.

minor comments (1)

The acronym expansion and precise architectural differences between TemPose-TF-ASF and the baseline TemPose are not clarified in the abstract, complicating immediate understanding of the incremental contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract and the two-stage training strategy. We address each major comment point by point below and outline the planned revisions.

read point-by-point responses

Referee: [—] Abstract: the central claim of 'consistent improvements' and 'notable performance gains' is asserted without any numerical values, error bars, dataset splits, ablation tables, or statistical significance tests. This prevents evaluation of whether the reported gains exceed baseline variance or are load-bearing for the two-stage ASF contribution.

Authors: We agree that the abstract would be strengthened by including specific quantitative results. In the revised version, we will update the abstract to report the key Accuracy and Macro-F1 improvements (with absolute deltas), reference the dataset splits, and point to the ablation tables and statistical tests already present in the experimental section of the full manuscript. revision: yes
Referee: [—] Abstract (two-stage strategy description): preliminary baseline predictions are reused as bidirectional context to 'guide the joint optimization' of ASF, yet no oracle ablation (ground-truth adjacent labels vs. predicted labels) or first-stage error-rate analysis is mentioned. If baseline error rates are non-negligible, any observed gains could partly compensate for propagated mistakes rather than demonstrate genuine context modeling; this is load-bearing for the method's validity.

Authors: This concern about error propagation is valid and load-bearing. The full manuscript contains ablations on the ASF module and transferability experiments, but lacks an explicit oracle comparison and first-stage error analysis. We will add both in the revision: (i) reported first-stage baseline error rates on the validation set, and (ii) an oracle ablation using ground-truth adjacent labels versus predicted labels to isolate the contribution of true context modeling. revision: yes

Circularity Check

0 steps flagged

No circularity: two-stage context reuse is standard bootstrapping, not definitional reduction

full rationale

The paper's core construction re-uses first-stage baseline predictions only as auxiliary input to train the ASF fusion weights; the final classifier output is not algebraically identical to those inputs, nor is any parameter fitted directly to the target metric by construction. No equations are presented that equate the reported Accuracy or Macro-F1 to the preliminary predictions, no self-citation supplies a uniqueness theorem, and no ansatz is smuggled in. The derivation therefore remains self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no equations, derivations, or technical specifications, preventing identification of free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5453 in / 944 out tokens · 34190 ms · 2026-05-08T18:14:03.803334+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

Cost.FunctionalEquation (washburn_uniqueness_aczel) J(x) = ½(x+x⁻¹)−1 uniqueness unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Param: 1.95M ... TemPose-TF-ASF achieves substantial performance improvements over the baseline TemPose-TF.

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Reference graph

Works this paper leans on

30 extracted references · 4 canonical work pages

[1]

arXiv preprint arXiv:2502.21085 (2025)

Chang, J.Y.: Bst: Badminton stroke-type transformer for skeleton-based action recognition in racket sports. arXiv preprint arXiv:2502.21085 (2025)

work page arXiv 2025
[2]

In: Proceedings of the 5th ACM International Conference on Multimedia in Asia

Chen, Y.J., Wang, Y.S.: Tracknetv3: Enhancing shuttlecock tracking with augmen- tations and trajectory rectification. In: Proceedings of the 5th ACM International Conference on Multimedia in Asia. pp. 1–7 (2023)

2023
[3]

In: Proceed- ings of the European conference on computer vision (ECCV)

Chen, Z., Huang, S., Tao, D.: Context refinement for object detection. In: Proceed- ings of the European conference on computer vision (ECCV). pp. 71–86 (2018)

2018
[4]

In: ECCV

Do, J., Kim, M.: Skateformer: skeletal-temporal transformer for human action recognition. In: ECCV. pp. 401–420. Springer (2025)

2025
[5]

In: 2019 international conference on image and video processing, and artificial intelligence

Dong, L., Li, D., Li, S., Lan, S., Wang, P.: Tai chi action recognition based on structural lstm with attention module. In: 2019 international conference on image and video processing, and artificial intelligence. vol. 11321, pp. 377–382. SPIE (2019)

2019
[6]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

Farha, Y.A., Gall, J.: Ms-tcn: Multi-stage temporal convolutional network for ac- tion segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

2019
[7]

In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S

Fernández, J., Bornn, L.: Soccermap: A deep learning architecture for visually- interpretable analysis in soccer. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds.) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. pp. 491–506. Springer International Pub- lishing, Cham (2021)

2021
[8]

In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

Ghosh, A., Singh, S., Jawahar, C.: Towards structured analysis of broadcast bad- minton videos. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 296–304. IEEE (2018) TemPose-TF-ASF 15

2018
[9]

In: Proceedings

Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm networks. In: Proceedings. 2005 IEEE International Joint Conference on Neu- ral Networks, 2005. vol. 4, pp. 2047–2052. IEEE (2005)

2005
[10]

Neural computation 9(8), 1735–1780 (1997)

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997)

1997
[11]

Applied Sciences11(10), 4499 (2021)

Huang, M.L., Li, Y.Z.: Use of machine learning and deep learning to predict the outcomes of major league baseball matches. Applied Sciences11(10), 4499 (2021)

2021
[12]

In: ProceedingsoftheIEEE/CVFConferenceonComputerVisionandPatternRecog- nition

Ibh, M., Grasshof, S., Witzner, D., Madeleine, P.: Tempose: a new skeleton-based transformer model designed for fine-grained motion recognition in badminton. In: ProceedingsoftheIEEE/CVFConferenceonComputerVisionandPatternRecog- nition. pp. 5199–5208 (2023)

2023
[13]

In: 2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC)

Jiang, K., Li, J., Liu, Z., Dong, C.: Court detection using masked perspective fields network. In: 2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC). pp. 342–345. IEEE (2023)

2023
[14]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Kulkarni, K.M., Shenoy, S.: Table tennis stroke recognition using two-dimensional human pose estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4576–4584 (2021)

2021
[15]

In: Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

Lea, C., Flynn, M.D., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks for action segmentation and detection. In: Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

2017
[16]

Mobile Information Systems2022(1), 3413584 (2022)

Liu, J., Liang, B.: An action recognition technology for badminton players using deep learning. Mobile Information Systems2022(1), 3413584 (2022)

2022
[17]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Liu, P., Wang, J.H.: Monotrack: Shuttle trajectory reconstruction from monocu- lar badminton video. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3513–3522 (2022)

2022
[18]

Journal of healthcare engineering2021(1), 7892902 (2021)

Ma, C., Yu, D., Feng, H.: [retracted] recognition of badminton shot action based on the improved hidden markov model. Journal of healthcare engineering2021(1), 7892902 (2021)

2021
[19]

In: JSAI International symposium on artificial intel- ligence

Nakai, M., Tsunoda, Y., Hayashi, H., Murakoshi, H.: Prediction of basketball free throw shooting by openpose. In: JSAI International symposium on artificial intel- ligence. pp. 435–446. Springer (2018)

2018
[20]

arXiv preprint arXiv:1511.08458 (2015)

O’shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015)

work page arXiv 2015
[21]

Computational Linguistics46(3), 667–712 (2020)

Oved, N., Feder, A., Reichart, R.: Predicting in-game actions from interviews of nba players. Computational Linguistics46(3), 667–712 (2020)

2020
[22]

In: Journal of Physics: Conference Series

Rahmad, N.A., As’ari, M.A.: The new convolutional neural network (cnn) local feature extractor for automated badminton action recognition on vision based data. In: Journal of Physics: Conference Series. vol. 1529, p. 022021. IOP Publishing (2020)

2020
[23]

Emerging Science Journal5(1), 25–33 (2021)

Sarabu, A., Santra, A.K.: Human action recognition in videos using convolution long short-term memory network with spatio-temporal networks. Emerging Science Journal5(1), 25–33 (2021)

2021
[24]

Advances in neural information processing systems27(2014)

Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems27(2014)

2014
[25]

Advanced Materials Research1042, 89–93 (2014)

Ting, H.Y., Sim, K.S., Abas, F.S.: Automatic badminton action recognition using rgb-d sensor. Advanced Materials Research1042, 89–93 (2014)

2014
[26]

Advances in neural information pro- cessing systems30(2017)

Vaswani,A.,Shazeer,N.,Parmar,N.,Uszkoreit,J.,Jones,L.,Gomez,A.N.,Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information pro- cessing systems30(2017)

2017
[27]

CoRRabs/2306.04948(2023) 16 Liu and Lee

Wang,W.,Huang,Y.,Ik,T.,Peng,W.:Shuttleset:Ahuman-annotatedstroke-level singles dataset for badminton tactical analysis. CoRRabs/2306.04948(2023) 16 Liu and Lee

work page arXiv 2023
[28]

In: Proceedings of the AAAI Conference on Artificial Intelligence

Wang, W.Y., Shuai, H.H., Chang, K.S., Peng, W.C.: Shuttlenet: Position-aware fusion of rally progress and player styles for stroke forecasting in badminton. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 4219– 4227 (2022)

2022
[29]

arXiv preprint arXiv:2412.09601 (2024)

Wang, X., Cheng, F., Wang, Z., Wang, H., Islam, M.M., Torresani, L., Bansal, M., Bertasius, G., Crandall, D.: Timerefine: Temporal grounding with time refining video llm. arXiv preprint arXiv:2412.09601 (2024)

work page arXiv 2024
[30]

In: CVPR (2024)

Zhou, Y., Yan, X., Cheng, Z.Q., Yan, Y., Dai, Q., Hua, X.S.: Blockgcn: Redefining topology awareness for skeleton-based action recognition. In: CVPR (2024)

2024