mlsanityData to 14 Oct 2026

Fine-grained Activity Recognition in Baseball Videos

arXiv 1804.03247 ↗First posted 9 Apr 2018cs.CVcited by 20

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Abstract (arXiv)

In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification as well as activity detection in continuous videos. We experimentally compare various recognition approaches capturing temporal structure in activity videos, by classifying segmented videos and extending those approaches to continuous videos. We also compare models on the extremely difficult task of predicting pitch speed and pitch type from broadcast baseball videos. We find that learning temporal structure is valuable for fine-grained activity recognition.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Apr 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 3; 2024: 3; 2025: 4; 2026: 1.

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  1. 2024FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessmentcited by 25
  2. 2021Recognizing Actions in Videos from Unseen Viewpointscited by 18
  3. 2022Exploring Fine-Grained Audiovisual Categorization with the SSW60 Datasetcited by 12
  4. 2023Tubelet-Contrastive Self-Supervision for Video-Efficient Generalizationcited by 12
  5. 2023A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decisioncited by 11
  6. 2025SoccerMaster: A Vision Foundation Model for Soccer Understandingcited by 10
  7. 2022Automatic Concept Extraction for Concept Bottleneck-based Video Classificationcited by 8
  8. 2022Weakly-Supervised Temporal Action Detection for Fine-Grained Videos with Hierarchical Atomic Actionscited by 8
  9. 2021Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learningcited by 7
  10. 20243D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approachcited by 7
  11. 2022Fine-grained Activities of People Worldwidecited by 4
  12. 2024LocoMotion: Learning Motion-Focused Video-Language Representationscited by 4
8 more
  1. 2023Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skatingcited by 3
  2. 2025EyePCR: A Comprehensive Benchmark for Fine-Grained Perception, Knowledge Comprehension and Clinical Reasoning in Ophthalmic Surgerycited by 3
  3. 2021Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understandingcited by 2
  4. 2022Recent Trends in 2D Object Detection and Applications in Video Event Recognitioncited by 0
  5. 2022How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbscited by 0
  6. 2025VIFSS: View-Invariant and Figure Skating-Specific Pose Representation Learning for Temporal Action Segmentationcited by 0
  7. 2025Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQAcited by 0
  8. 2026Event-based Batting Impact Estimationcited by 0