🥬 "Perception Test" by #DeepMind 🥬

👉Huge dataset with obj & point tracks, temporal sounds, multiple & grounded vQA

😎Review https://bit.ly/3Vqh96Q
😎Dataset github.com/deepmind/perception_test
😎Project www.deepmind.com/blog/measuring-perception-in-ai-models

🏇 Bootstrapping TAP 🏇

👉#Deepmind shows how large-scale, unlabeled, uncurated real-world data can improve TAP with minimal architectural changes, via a self-supervised student-teacher setup. Source Code released 💙

👉Review https://t.ly/-S_ZL
👉Paper arxiv.org/pdf/2402.00847.pdf
👉Code https://github.com/google-deepmind/tapnet

🍾TAPVid-3D: benchmark for TAP-3D🍾

👉#Deepmind (+College London & Oxford) introduces TAPVid-3D, a new benchmark for evaluating long-range Tracking Any Point in 3D: 4,000+ real-world videos, composed of three different data sources spanning a variety of object types, motion patterns, and indoor/outdoor environments. Data & Code available, Apache 2.0💙

👉Review https://t.ly/SsptD
👉Paper arxiv.org/pdf/2407.05921
👉Project tapvid3d.github.io/
👉Code github.com/google-deepmind/tapnet/tree/main/tapnet/tapvid3d