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Found 13 papers, 8 papers with code
DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body
Learning to locomote when the length and the thickness of different body parts vary is challenging, as the control policy is required to adapt to the morphology to successfully balance and advance the agent.
Panoptic animal pose estimators are zero-shot performers
The models can be further improved by (pseudo) labeled fine-tuning.
Seeing biodiversity: perspectives in machine learning for wildlife conservation
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices.
AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild
Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging.
Measuring and modeling the motor system with machine learning
The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data.
End-to-End Trainable Multi-Instance Pose Estimation with Transformers
Inspired by recent work on end-to-end trainable object detection with transformers, we use a transformer encoder-decoder architecture together with a bipartite matching scheme to directly regress the pose of all individuals in a given image.
Out-of-distribution generalization of internal models is correlated with reward
We investigate the behavior of reinforcement learning (RL) agents under morphological distribution shifts.
Out-of-Distribution Generalization
reinforcement-learning
+1
Perspectives on individual animal identification from biology and computer vision
Identifying individual animals is crucial for many biological investigations.
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem.
ImageNet performance correlates with pose estimation robustness and generalization on out-of-domain data
Neural networks are highly effective tools for pose estimation.
Deep learning tools for the measurement of animal behavior in neuroscience
Recent advances in computer vision have made accurate, fast and robust measurement of animal behavior a reality.
Pretraining boosts out-of-domain robustness for pose estimation
Neural networks are highly effective tools for pose estimation.
Ranked #1 on
Animal Pose Estimation
on Horse-10
Markerless tracking of user-defined features with deep learning
Quantifying behavior is crucial for many applications in neuroscience.
