mlsanityData to 14 Oct 2026

A Review on Coarse to Fine-Grained Animal Action Recognition

arXiv 2506.01214 ↗First posted 1 Jun 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

This review provides an in-depth exploration of the field of animal action recognition, focusing on coarse-grained (CG) and fine-grained (FG) techniques. The primary aim is to examine the current state of research in animal behaviour recognition and to elucidate the unique challenges associated with recognising subtle animal actions in outdoor environments. These challenges differ significantly from those encountered in human action recognition due to factors such as non-rigid body structures, frequent occlusions, and the lack of large-scale, annotated datasets. The review begins by discussing the evolution of human action recognition, a more established field, highlighting how it progressed from broad, coarse actions in controlled settings to the demand for fine-grained recognition in dynamic environments. This shift is particularly relevant for animal action recognition, where behavioural variability and environmental complexity present unique challenges that human-centric models cannot fully address. The review then underscores the critical differences between human and animal action recognition, with an emphasis on high intra-species variability, unstructured datasets, and the natural complexity of animal habitats. Techniques like spatio-temporal deep learning frameworks (e.g., SlowFast) are evaluated for their effectiveness in animal behaviour analysis, along with the limitations of existing datasets. By assessing the strengths and weaknesses of current methodologies and introducing a recently-published dataset, the review outlines future directions for advancing fine-grained action recognition, aiming to improve accuracy and generalisability in behaviour analysis across species.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 71 references that are in the map. First posted on arXiv 1 Jun 2025.

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29 of 71 references.

References · 29

  1. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
  2. 2018SlowFast Networks for Video Recognitioncited by 1,867
  3. 2020X3D: Expanding Architectures for Efficient Video Recognitioncited by 637
  4. 2015Towards Open Set Deep Networkscited by 538
  5. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 538
  6. 2021MViTv2: Improved Multiscale Vision Transformers for Classification and Detectioncited by 519
  7. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 508
  8. 2019MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentationcited by 373
  9. 2023AIM: Adapting Image Models for Efficient Video Action Recognitioncited by 148
  10. 2022Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understandingcited by 103
  11. 2023Diffusion Action Segmentationcited by 74
  12. 2021Temporal Query Networks for Fine-grained Video Understandingcited by 68
17 more
  1. 2018Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challengecited by 44
  2. 2023MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understandingcited by 36
  3. 2021Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classificationcited by 31
  4. 2021AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wildcited by 30
  5. 2021Learning from Temporal Gradient for Semi-supervised Action Recognitioncited by 26
  6. 2020Feedback Graph Convolutional Network for Skeleton-based Action Recognitioncited by 22
  7. 2022SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorizationcited by 21
  8. 2023DiffTAD: Temporal Action Detection with Proposal Denoising Diffusioncited by 19
  9. 2022Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognitioncited by 17
  10. 2022OpenApePose: a database of annotated ape photographs for pose estimationcited by 10
  11. 2021Florida Wildlife Camera Trap Datasetcited by 7
  12. 2021Video-based cattle identification and action recognitioncited by 7
  13. 2022Adjusting for Bias with Procedural Datacited by 6
  14. 2023Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognitioncited by 4
  15. 2023CVB: A Video Dataset of Cattle Visual Behaviorscited by 4
  16. 2023Proposal-based Temporal Action Localization with Point-level Supervisioncited by 2
  17. 2023Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networkscited by 1

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