Training a simple classifier on top of "frozen features" from large vision models is now common and powerful. In our CVPR 2024 paper (https://t.co/uF0PXbetD3), we show that just applying simple augmentations on such frozen features can improve few-shot classification.
I’m presenting “Agents’ Room: Narrative Generation through Multi-step Collaboration” at the #ICLR2025 poster session at 3pm today.
come chat about multi-agent systems for complex writing tasks!
link: https://t.co/AxqqQlgqqM
paper: https://t.co/FSIRvSXT37
🔥Excited to introduce RINS - a technique that boosts model performance by recursively applying early layers during inference without increasing model size or training compute flops! Not only does it significantly improve LMs, but also multimodal systems like SigLIP.
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Want your VLM to reflect the world's rich diversity 🌍? We’re very excited to share our recent research on this topic. TLDR: to build truly inclusive models that work for everyone, don’t filter by English, and check out our recommended evaluation benchmarks. (1/7)
Training a simple classifier on top of "frozen features" from large vision models is now common and powerful. In our CVPR 2024 paper (https://t.co/uF0PXbetD3), we show that just applying simple augmentations on such frozen features can improve few-shot classification.