mlsanityData to 14 Oct 2026

TennisTV: Do Multimodal Large Language Models Understand Tennis Rallies?

arXiv 2509.15602 ↗First posted 19 Sep 2025cs.CVcited by 3

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

Multimodal large language models (MLLMs) excel at general video understanding but struggle with fast, high-frequency sports like tennis, where rally clips are short yet information-dense. To systematically evaluate MLLMs in this challenging domain, we present TennisTV, the first and most comprehensive benchmark for tennis video understanding. TennisTV models each rally as a temporal-ordered sequence of consecutive stroke events, using automated pipelines for filtering and question generation. It covers 8 tasks from the stroke level to the rally level and includes 2527 human-verified questions. Evaluating 17 representative MLLMs, we provide the first systematic assessment of tennis video understanding. Results yield two key insights: (i) frame-sampling density should be tailored and balanced across tasks, and (ii) improving temporal grounding is essential for stronger reasoning.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 21 of its 26 references that are in the map. First posted on arXiv 19 Sep 2025.

Builds on

21 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models11 Oct

Other references · 20

  1. 2023Visual Instruction Tuningcited by 7,869
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  3. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  4. 2025Qwen2.5-VL Technical Reportcited by 5,587
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  6. 2024Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysiscited by 1,413
  7. 2023Video-LLaVA: Learning United Visual Representation by Alignment Before Projectioncited by 1,402
  8. 2023Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Modelscited by 1,062
  9. 2025Video-R1: Reinforcing Video Reasoning in MLLMscited by 484
  10. 2024mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Modelscited by 291
  11. 2025VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuningcited by 255
  12. 2020FineGym: A Hierarchical Video Dataset for Fine-grained Action Understandingcited by 234
8 more
  1. 2025InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modelingcited by 211
  2. 2020SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videoscited by 131
  3. 2025MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttrainingcited by 121
  4. 2025VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuningcited by 93
  5. 2024Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sportscited by 41
  6. 2024From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understandingcited by 30
  7. 2024SportQA: A Benchmark for Sports Understanding in Large Language Modelscited by 15
  8. 2025F3Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videoscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025DeepSport: A Multimodal Large Language Model for Comprehensive Sports Video Reasoning via Agentic Reinforcement Learningcited by 5
  2. 2026Watch, Remember, Reason: Human-View Video Understanding with MLLMscited by 4
  3. 2026Learning Consistent Temporal Grounding between Related Tasks in Sports Coachingcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026TennisVAR: A Stroke-Evidence-Grounded Multimodal Large Language Model for Tactical Reasoning in Tennis Videos
  2. 2026Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding
  3. 2025SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports
  4. 2026Towards Temporal Compositional Reasoning in Long-Form Sports Videos
  5. 2024Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video
  6. 2025Enhancing Sports Strategy with Video Analytics and Data Mining: Assessing the effectiveness of Multimodal LLMs in tennis video analysis
  7. 2025FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding
  8. 2025MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues