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🚀A Simple Baseline for Streaming Video Understanding🚀 #SimpleStream reveals a simple baseline of recent N-frame sliding window beats SOTA memory-based methods on standard streaming video benchmarks lmms-lab - Project: - Code:

Ziwei Liu

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36,054 views • 3 months ago •via X (Twitter)

Education News & Politics Science & Technology #SimpleStream

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18,789 views • 7 months ago

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𝐀𝐍𝐎𝐍𝐆 𝐏𝐑𝐎𝐁𝐋𝐄𝐌𝐀 𝐌𝐎 𝐄𝐇 𝐌𝐀𝐇𝐀𝐋 𝐃𝐈𝐍 𝐊𝐈𝐓𝐀 As we enter a 𝗻𝗲𝘄 𝗲𝗿𝗮 of 𝙔𝙤𝙪’𝙧𝙚 𝙈𝙮 𝙁𝙖𝙫𝙤𝙧𝙞𝙩𝙚 𝙎𝙤𝙣𝙜, we just want to sincerely thank everyone for the love and support you’ve shown. Thank you for streaming the 𝙏𝙚𝙠𝙖 𝙇𝙖𝙣𝙜 music video and helping it reach 𝟭.𝟳𝗠 𝘃𝗶𝗲𝘄𝘀 𝗮𝗻𝗱 𝗰𝗼𝘂𝗻𝘁𝗶𝗻𝗴. We truly appreciate every single one of you who took the time to watch, stream, and support the project. As a small way of giving back, here’s our little 𝙖𝙮𝙪𝙙𝙖 for all of you, a 𝗧𝗶𝗸𝗧𝗼𝗸 𝘃𝗶𝗱𝗲𝗼 𝗳𝗲𝗮𝘁𝘂𝗿𝗶𝗻𝗴 𝗔𝗻𝗴𝗲𝗹. We hope you enjoy it! 🥂 The question is, 𝙗𝙖𝙠𝙞𝙩 𝙠𝙖 𝙣𝙖𝙜𝙠𝙖𝙠𝙖𝙜𝙖𝙣𝙞𝙮𝙖𝙣? 𝙔𝙤𝙪’𝙧𝙚 𝙈𝙮 𝙁𝙖𝙫𝙤𝙧𝙞𝙩𝙚 𝙎𝙤𝙣𝙜, this 𝐉𝐮𝐧𝐞 on 𝐆𝐌𝐀.

TEAM CAPRICE OFFICIAL

63,952 views • 1 month ago

🚀 Excited to share our #ICLR2025 work on planning with neural dynamics models! While our lab has developed diverse neural dynamics models for manipulating rigid, deformable, and granular objects, having the model alone doesn’t solve the problem—planning with it remains a challenge. 💡 Enter BaB-ND, led by Keyi and Jiangwei! We propose a scalable, GPU-accelerated branch-and-bound algorithm, inspired by neural network verification, to enable effective planning for diverse objects modeled with neural dynamics. 🔗 Project page (open-source + detailed docs!): 🎥 Watch the video to see T being pushed around obstacles, and check out Keyi’s thread for more details!

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Yunzhu Li

10,561 views • 1 year ago

Streaming iPhone data in real-time directly to Rerun 🚀 The collection process is one of the most frustrating parts of building imitation-learning datasets. I’ve got a little army of sensors—📱 iPhone, iPad, Quest 3—but getting them temporally aligned, spatially aligned, AND seeing real-time feedback while recording is tough. I stumbled on a great library from Cake Lab (WPI) called ARFlow. It’s a thin client built on Unity’s ARFoundation that connects over gRPC to a server running Rerun for live data logging. I forked it to: - Log the SLAM translation poses, and - Upgrade rerun to v0.23 for my use case. So far, it works well, but there are still a few hitches: 1. Right now, it’s solid on iPhone and iPad; my Quest 3 client is still slow and not super reliable. 2. I’m using an older ARFlow branch focused on real-time streaming only—no spatial or temporal sync yet. Unity builds for iOS keep failing. 🛠️ 3. Nothing is saved locally to the client, so packet loss is a risk on shaky networks. There’s huge potential in tapping the ubiquitous sensors we carry around every day, and ARFlow is a big step toward making that easy

Streaming iPhone data in real-time directly to Rerun 🚀 The collection process is one of the most frustrating parts of building imitation-learning datasets. I’ve got a little army of sensors—📱 iPhone, iPad, Quest 3—but getting them temporally aligned, spatially aligned, AND seeing real-time feedback while recording is tough. I stumbled on a great library from Cake Lab (WPI) called ARFlow. It’s a thin client built on Unity’s ARFoundation that connects over gRPC to a server running Rerun for live data logging. I forked it to: - Log the SLAM translation poses, and - Upgrade rerun to v0.23 for my use case. So far, it works well, but there are still a few hitches: 1. Right now, it’s solid on iPhone and iPad; my Quest 3 client is still slow and not super reliable. 2. I’m using an older ARFlow branch focused on real-time streaming only—no spatial or temporal sync yet. Unity builds for iOS keep failing. 🛠️ 3. Nothing is saved locally to the client, so packet loss is a risk on shaky networks. There’s huge potential in tapping the ubiquitous sensors we carry around every day, and ARFlow is a big step toward making that easy

Pablo Vela

27,383 views • 1 year ago