Pavankumar Vasu @PavankumarVasu
Joined July 2013-
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📢 FastVLM models are now on 🤗
📢 Releasing MobileCLIP2 (TMLR Featured). Small embedding models that can power your multimodal RAG applications on resource constrained devices. Models are available on 🤗
📢 Releasing MobileCLIP2 (TMLR Featured). Small embedding models that can power your multimodal RAG applications on resource constrained devices. Models are available on 🤗
🚀Releasing MobileCLIP2 (TMLR Featured). MobileCLIP2-S4 matches acc of SigLIP-SO400M/14 while 2x smaller and surpasses DFN ViT-L/14 at 2.5x faster. Paper: arxiv.org/abs/2508.20691 Code: github.com/apple/ml-mobil… RayGen: github.com/apple/ml-mobil… 🤗huggingface.co/collections/ap… #Apple MLR
🚨📅The submission deadline for #NeurIPS 2025 CCFM Workshop is just 8 days away on August 22. Get your papers in! Submit your work on Continual and Compatible Foundation Model Updates to the #NeurIPS 2025 CCFM Workshop. Learn more: sites.google.com/view/ccfm-neur…
🚨📅The submission deadline for #NeurIPS 2025 CCFM Workshop is just 8 days away on August 22. Get your papers in! Submit your work on Continual and Compatible Foundation Model Updates to the #NeurIPS 2025 CCFM Workshop. Learn more: sites.google.com/view/ccfm-neur…
Introducing DINOv3 🦕🦕🦕 A SotA-enabling vision foundation model, trained with pure self-supervised learning (SSL) at scale. High quality dense features, combining unprecedented semantic and geometric scene understanding. Three reasons why this matters…
🚀 We're thrilled to launch four new OCR datasets with 20M images: DoclingMatix, SynthFormulaNet, SynthCodeNet, and SynthChartNet. We used them train SmolDocling, our ultra‑compact (256M) full-page document conversion VLM with performance rivaling models up to 27× larger.
Uncertainty quantification (UQ) is key for safe, reliable LLMs... but are we evaluating it correctly? 🚨 Our ACL2025 paper finds a hidden flaw: if both UQ methods and correctness metrics are biased by the same factor (e.g., response length), evaluations get systematically skewed
🌟Explore key insights from the FastVLM project (real-time vision-language model) in this blog post: machinelearning.apple.com/research/fast-…
📢Submissions are now open for #NeurIPS2025 CCFM workshop. Submission deadline: August 22, 2025, AoE. Website: sites.google.com/view/ccfm-neur… Call for papers: sites.google.com/view/ccfm-neur… Submission Link: openreview.net/group?id=NeurI…
📢Submissions are now open for #NeurIPS2025 CCFM workshop. Submission deadline: August 22, 2025, AoE. Website: sites.google.com/view/ccfm-neur… Call for papers: sites.google.com/view/ccfm-neur… Submission Link: openreview.net/group?id=NeurI…
We propose new scaling laws that predict the optimal data mixture, for pretraining LLMs, native multimodal models and large vision encoders ! Only running small-scale experiments is needed, and we can then extrapolate to large-scale ones. These laws allow 1/n 🧵
📣 We are excited to present our work on inferring user preferences from writing samples at @icmlconf Poster Session 3 (Wed. 11:00AM - 1:30PM)! Come by to ✋ chat with us, 📄 learn about our method, and 💻 hear about our new interactive benchmark (🔗s below)!
🚀Super excited to share TiC-LM (Oral at #ACL2025)! How to keep FMs up-to-date over months/years? We have a benchmark and lots of insights (arxiv.org/abs/2504.02107). Also organizing a related @NeurIPSConf 2025 workshop continual and compatible FMs (CCFM: sites.google.com/view/ccfm-neur…)…
🚀Super excited to share TiC-LM (Oral at #ACL2025)! How to keep FMs up-to-date over months/years? We have a benchmark and lots of insights (arxiv.org/abs/2504.02107). Also organizing a related @NeurIPSConf 2025 workshop continual and compatible FMs (CCFM: sites.google.com/view/ccfm-neur…)…
I will be attending #CVPR2025 and presenting our latest research at Apple MLR! Specifically, I will present our highlight poster--world consistent video diffusion (cvpr.thecvf.com/virtual/2025/p…), and three workshop invited talks which includes our recent preprint ★STARFlow★! (0/n)
I will be attending #CVPR2025 and presenting our latest research at Apple MLR! Specifically, I will present our highlight poster--world consistent video diffusion (cvpr.thecvf.com/virtual/2025/p…), and three workshop invited talks which includes our recent preprint ★STARFlow★! (0/n)
Imitation learning has a data scarcity problem. Introducing EgoDex from Apple, the largest and most diverse dataset of dexterous human manipulation to date — 829 hours of egocentric video + paired 3D hand poses across 194 tasks. Now on arxiv: arxiv.org/abs/2505.11709 (1/4)
Excited to introduce FocalLens: an instruction tuning framework that turns existing VLMs/MLLMs into text-conditioned vision encoders that produce visual embeddings focusing on relevant visual information given natural language instructions! 📢: @HPouransari will be presenting…
Here is an RL perspective on understanding LLMs for decision making. Are LLMs best used as: policies / rewards / transition functions ? How do you fine-tune them ? Can LLMs explore / exploit ? 🧵 Join us down this rabbit hole... (ICLR 2025 paper, done at ML Research)
Excited to share our new paper on "Reversal Blessing" - where thinking BACKWARDS makes language models smarter on some multiple-choice questions! We found that right-to-left (R2L) models consistently outperform traditional left-to-right (L2R) models on certain reasoning tasks.🧵
🚨 One question that has always intrigued me is the role of different ways to increase a model's capacity: parameters, parallelizable compute, or sequential compute? We explored this through the lens of MoEs:
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