We pushed FERAL to handle multi-species, multi-behavior field recordings: day and night, dense vegetation, variable lighting.
FERAL achieves strong accuracy even for visually rare but distinct behaviors like climbing up/down.
We pushed FERAL to handle multi-species, multi-behavior field recordings: day and night, dense vegetation, variable lighting.
FERAL achieves strong accuracy even for visually rare but distinct behaviors like climbing up/down.
Next we wanted to try FERAL on recordings of social behavior. FERAL detects both self-grooming and allogrooming directly from raw video, even with occlusions and two animals with very different shapes.
Next we wanted to try FERAL on recordings of social behavior. FERAL detects both self-grooming and allogrooming directly from raw video, even with occlusions and two animals with very different shapes.
🪱 C. elegans locomotion (with Friederike Buck)
FERAL incorporates temporal dynamics in its learning. In this dataset, it separates forward and reverse crawling: behaviors that look nearly identical frame-to-frame, but are revealed by temporal dynamics!
🪱 C. elegans locomotion (with Friederike Buck)
FERAL incorporates temporal dynamics in its learning. In this dataset, it separates forward and reverse crawling: behaviors that look nearly identical frame-to-frame, but are revealed by temporal dynamics!
On benchmarks, FERAL beats both pose- and video-based baselines: 94.5% mAP on CalMS21 (mouse social interactions). Outperforms Google’s VideoPrism while using only 25% of the data.
Here’s a snippet of FERAL segmenting mouse social behavior:
On benchmarks, FERAL beats both pose- and video-based baselines: 94.5% mAP on CalMS21 (mouse social interactions). Outperforms Google’s VideoPrism while using only 25% of the data.
Here’s a snippet of FERAL segmenting mouse social behavior: