# MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps

arXiv: https://arxiv.org/abs/2503.18223 · HTML: https://mlsanity.com/paper/2503.18223/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 58 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Token Merging: Your ViT But Faster](https://mlsanity.com/paper/2210.09461/) (2022)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [InternVideo: General Video Foundation Models via Generative and Discriminative Learning](https://mlsanity.com/paper/2212.03191/) (2022)
- [A Short Note on the Kinetics-700 Human Action Dataset](https://mlsanity.com/paper/1907.06987/) (2019)
- [Masked Autoencoders that Listen](https://mlsanity.com/paper/2207.06405/) (2022)
- [Learning Video Representations from Large Language Models](https://mlsanity.com/paper/2212.04501/) (2022)
- [Audiovisual SlowFast Networks for Video Recognition](https://mlsanity.com/paper/2001.08740/) (2020)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding](https://mlsanity.com/paper/2204.08129/) (2022)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding](https://mlsanity.com/paper/2310.19060/) (2023)
- [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://mlsanity.com/paper/1912.03538/) (2019)
- [Deep learning tools for the measurement of animal behavior in neuroscience](https://mlsanity.com/paper/1909.13868/) (2019)
- [TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval](https://mlsanity.com/paper/2409.01156/) (2024)
- [SuperAnimal pretrained pose estimation models for behavioral analysis](https://mlsanity.com/paper/2203.07436/) (2022)
- [MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding](https://mlsanity.com/paper/2306.00576/) (2023)
- [ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors](https://mlsanity.com/paper/2310.16447/) (2023)
- [PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition](https://mlsanity.com/paper/2401.13554/) (2024)
- [On the Utility of 3D Hand Poses for Action Recognition](https://mlsanity.com/paper/2403.09805/) (2024)
- [TIM: A Time Interval Machine for Audio-Visual Action Recognition](https://mlsanity.com/paper/2404.05559/) (2024)
- [Triple-stream Deep Metric Learning of Great Ape Behavioural Actions](https://mlsanity.com/paper/2301.02642/) (2023)
- [Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation](https://mlsanity.com/paper/2405.12930/) (2024)
- [BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos](https://mlsanity.com/paper/2405.17698/) (2024)
- [ChimpVLM: Ethogram-Enhanced Chimpanzee Behaviour Recognition](https://mlsanity.com/paper/2404.08937/) (2024)
- [Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos](https://mlsanity.com/paper/2501.08931/) (2025)

## Built on by (most cited first, 14 of 14)

- [The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification](https://mlsanity.com/paper/2511.15622/) (2025)
- [PriVi: Towards A General-Purpose Video Model For Primate Behavior In The Wild](https://mlsanity.com/paper/2511.09675/) (2025)
- [VIOLA: Towards Video In-Context Learning with Minimal Annotations](https://mlsanity.com/paper/2601.15549/) (2026)
- [Grounding Intelligence in Movement](https://mlsanity.com/paper/2507.02771/) (2025)
- [WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation](https://mlsanity.com/paper/2603.16816/) (2026)
- [Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data](https://mlsanity.com/paper/2607.09876/) (2026)
- [MSC: A Marine Wildlife Video Dataset with Grounded Segmentation and Clip-Level Captioning](https://mlsanity.com/paper/2508.04549/) (2025)
- [SmartWilds: Multimodal Wildlife Monitoring Dataset](https://mlsanity.com/paper/2509.18894/) (2025)
- [Multi-Object Tracking Consistently Improves Wildlife Inference](https://mlsanity.com/paper/2605.16672/) (2026)
- [Explainable Forensics of Manipulated Segments in Untrimmed Long Videos](https://mlsanity.com/paper/2606.02402/) (2026)
- [Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?](https://mlsanity.com/paper/2606.03837/) (2026)
- [Cross-Modal Corroboration for Annotation-Free Wildlife Monitoring](https://mlsanity.com/paper/2606.21613/) (2026)
- [Teach a Molmo2Fish: Towards interactive fish tracking with natural language guidance](https://mlsanity.com/paper/2608.18602/) (2026)
- [WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition](https://mlsanity.com/paper/2608.21281/) (2026)

## Abstract (arXiv)

Monitoring wildlife is essential for ecology and ethology, especially in light of the increasing human impact on ecosystems. Camera traps have emerged as habitat-centric sensors enabling the study of wildlife populations at scale with minimal disturbance. However, the lack of annotated video datasets limits the development of powerful video understanding models needed to process the vast amount of fieldwork data collected. To advance research in wild animal behavior monitoring we present MammAlps, a multimodal and multi-view dataset of wildlife behavior monitoring from 9 camera-traps in the Swiss National Park. MammAlps contains over 14 hours of video with audio, 2D segmentation maps and 8.5 hours of individual tracks densely labeled for species and behavior. Based on 6135 single animal clips, we propose the first hierarchical and multimodal animal behavior recognition benchmark using audio, video and reference scene segmentation maps as inputs. Furthermore, we also propose a second ecology-oriented benchmark aiming at identifying activities, species, number of individuals and meteorological conditions from 397 multi-view and long-term ecological events, including false positive triggers. We advocate that both tasks are complementary and contribute to bridging the gap between machine learning and ecology. Code and data are available at: https://github.com/eceo-epfl/MammAlps