733 citations · 2.2k across the 19 of their papers we have counts for
12 papers · 1 filter
PRIMA: Boosting Animal Mesh Recovery with Taxonomic Priors and Adaptation-Driven Data Bootstrapping
Xiaohang Yu, Ti Wang, Mackenzie Weygandt Mathis
Recovering articulated 3D animal meshes from monocular images is challenging due to the long-tailed distribution of species and limited large-scale 3D supervision. Existing methods…
FMPose3D: monocular 3D pose estimation via flow matching
Ti Wang, Xiaohang Yu, Mackenzie Weygandt Mathis
Monocular 3D pose estimation is fundamentally ill-posed due to depth ambiguity and occlusions, thereby motivating probabilistic methods that generate multiple plausible 3D pose hyp…
DISTIL: Data-Free Inversion of Suspicious Trojan Inputs via Latent Diffusion
Hossein Mirzaei, Zeinab Taghavi, Sepehr Rezaee +3
Deep neural networks have demonstrated remarkable success across numerous tasks, yet they remain vulnerable to Trojan (backdoor) attacks, raising serious concerns about their safet…
LLaVAction: evaluating and training multi-modal large language models for action understanding
Haozhe Qi, Shaokai Ye, Alexander Mathis +1
Understanding human behavior requires measuring behavioral actions. Due to its complexity, behavior is best mapped onto a rich, semantic structure such as language. Emerging multim…
A Contrastive Teacher-Student Framework for Novelty Detection under Style Shifts
Hossein Mirzaei, Mojtaba Nafez, Moein Madadi +12
There have been several efforts to improve Novelty Detection (ND) performance. However, ND methods often suffer significant performance drops under minor distribution shifts caused…
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
Mu Zhou, Lucas Stoffl, Mackenzie Weygandt Mathis +1
Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms. Current pipelines either use an object detector together with a pose estimator…
SuperAnimal pretrained pose estimation models for behavioral analysis
Shaokai Ye, Anastasiia Filippova, Jessy Lauer +5
Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. A common key step for behavioral analysis is…
AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild
Daniel Joska, Liam Clark, Naoya Muramatsu +5
Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging. Being abl…
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
Alexander Mathis, Steffen Schneider, Jessy Lauer +1
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously adva…
Deep learning tools for the measurement of animal behavior in neuroscience
Mackenzie W. Mathis, Alexander Mathis
Recent advances in computer vision have made accurate, fast and robust measurement of animal behavior a reality. In the past years powerful tools specifically designed to aid the m…
Pretraining boosts out-of-domain robustness for pose estimation
Alexander Mathis, Thomas Biasi, Steffen Schneider +4
Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for sma…
Markerless tracking of user-defined features with deep learning
Alexander Mathis, Pranav Mamidanna, Taiga Abe +4
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, y…