# BFMD: A Full-Match Badminton Dense Dataset for Dense Shot Captioning

arXiv: https://arxiv.org/abs/2603.25533 · HTML: https://mlsanity.com/paper/2603.25533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 48 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 25 of 25)

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- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
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- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [InternVideo2: Scaling Foundation Models for Multimodal Video Understanding](https://mlsanity.com/paper/2403.15377/) (2024)
- [Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning](https://mlsanity.com/paper/2302.14115/) (2023)
- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [MatchTime: Towards Automatic Soccer Game Commentary Generation](https://mlsanity.com/paper/2406.18530/) (2024)
- [TTNet: Real-time temporal and spatial video analysis of table tennis](https://mlsanity.com/paper/2004.09927/) (2020)
- [TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications](https://mlsanity.com/paper/1907.03698/) (2019)
- [GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation](https://mlsanity.com/paper/2303.14655/) (2023)
- [Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation](https://mlsanity.com/paper/2104.09907/) (2021)
- [X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model](https://mlsanity.com/paper/2404.06332/) (2024)
- [Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton](https://mlsanity.com/paper/2211.12217/) (2022)
- [FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding](https://mlsanity.com/paper/2508.07554/) (2025)
- [Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset](https://mlsanity.com/paper/2306.15664/) (2023)
- [Going for GOAL: A Resource for Grounded Football Commentaries](https://mlsanity.com/paper/2211.04534/) (2022)
- [Estimation of control area in badminton doubles with pose information from top and back view drone videos](https://mlsanity.com/paper/2305.04247/) (2023)
- [Pose2Trajectory: Using Transformers on Body Pose to Predict Tennis Player's Trajectory](https://mlsanity.com/paper/2411.04501/) (2024)
- [Shot2Tactic-Caption: Multi-Scale Captioning of Badminton Videos for Tactical Understanding](https://mlsanity.com/paper/2510.14617/) (2025)

## Built on by (most cited first, 1 of 1)

- [DShuttleNet: A Badminton Doubles Dataset for Evaluating Team Coordination](https://mlsanity.com/paper/2610.14016/) (2026)

## Abstract (arXiv)

Understanding tactical dynamics in badminton requires analyzing entire matches rather than isolated clips. However, existing badminton datasets mainly focus on short clips or task-specific annotations and rarely provide full-match data with dense multimodal annotations. This limitation makes it difficult to generate accurate shot captions and perform match-level analysis. To address this limitation, we introduce the first Badminton Full Match Dense (BFMD) dataset, with 19 broadcast matches (including both singles and doubles) covering over 20 hours of play, comprising 1,687 rallies and 16,751 hit events, each annotated with a shot caption. The dataset provides hierarchical annotations including match segments, rally events, and dense rally-level multimodal annotations such as shot types, shuttle trajectories, player pose keypoints, and shot captions. We develop a VideoMAE-based multimodal captioning framework with a Semantic Feedback mechanism that leverages shot semantics to guide caption generation and improve semantic consistency. Experimental results demonstrate that multimodal modeling and semantic feedback improve shot caption quality over RGB-only baselines. We further showcase the potential of BFMD by analyzing the temporal evolution of tactical patterns across full matches.