# A Review on Coarse to Fine-Grained Animal Action Recognition

arXiv: https://arxiv.org/abs/2506.01214 · HTML: https://mlsanity.com/paper/2506.01214/

## 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.

## Builds on (most cited first, 29 of 29)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [AIM: Adapting Image Models for Efficient Video Action Recognition](https://mlsanity.com/paper/2302.03024/) (2023)
- [Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding](https://mlsanity.com/paper/2204.08129/) (2022)
- [Diffusion Action Segmentation](https://mlsanity.com/paper/2303.17959/) (2023)
- [Temporal Query Networks for Fine-grained Video Understanding](https://mlsanity.com/paper/2104.09496/) (2021)
- [Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge](https://mlsanity.com/paper/1807.05812/) (2018)
- [MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding](https://mlsanity.com/paper/2306.00576/) (2023)
- [Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification](https://mlsanity.com/paper/2101.06635/) (2021)
- [AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild](https://mlsanity.com/paper/2103.13282/) (2021)
- [Learning from Temporal Gradient for Semi-supervised Action Recognition](https://mlsanity.com/paper/2111.13241/) (2021)
- [Feedback Graph Convolutional Network for Skeleton-based Action Recognition](https://mlsanity.com/paper/2003.07564/) (2020)
- [SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization](https://mlsanity.com/paper/2209.02109/) (2022)
- [DiffTAD: Temporal Action Detection with Proposal Denoising Diffusion](https://mlsanity.com/paper/2303.14863/) (2023)
- [Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition](https://mlsanity.com/paper/2209.01425/) (2022)
- [OpenApePose: a database of annotated ape photographs for pose estimation](https://mlsanity.com/paper/2212.00741/) (2022)
- [Florida Wildlife Camera Trap Dataset](https://mlsanity.com/paper/2106.12628/) (2021)
- [Video-based cattle identification and action recognition](https://mlsanity.com/paper/2110.07103/) (2021)
- [Adjusting for Bias with Procedural Data](https://mlsanity.com/paper/2204.01108/) (2022)
- [Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition](https://mlsanity.com/paper/2302.13434/) (2023)
- [CVB: A Video Dataset of Cattle Visual Behaviors](https://mlsanity.com/paper/2305.16555/) (2023)
- [Proposal-based Temporal Action Localization with Point-level Supervision](https://mlsanity.com/paper/2310.05511/) (2023)
- [Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks](https://mlsanity.com/paper/2302.02755/) (2023)

## 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.