FunAI

11 posts

FunAI

FunAI

@FunAILab

Research lab led by @y_m_asano at @utn_nuremberg. We conduct fundamental AI research and develop core technology for future Foundation Models.

Nuremberg Beigetreten Temmuz 2024
88 Folgt228 Follower
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Yuki
Yuki@y_m_asano·
Happy to share what we've been cooking! 🎊🎊 Our next iteration of the ELLIS PhD school is set to be an *absolutely amazing* one with this stellar lineup of speakers. ..And we even have some more speakers to be confirmed. 👀 If you haven't yet, go apply :)
ELLIS Amsterdam@Ellis_Amsterdam

🚀 Two Big Announcements for ELLIS | ELIAS | ELLIOT Winter School on Foundation Models (FoMo) 2026, which is organised at the Tolhuistuin from 24-27 March 2026! 1️⃣ We’re excited to reveal the incredible speaker lineup for FoMo 2026... (1)

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Yuki
Yuki@y_m_asano·
@FunAILab + CVMP Lab of @EddyIlg retreat: ☑. From mountains to hackathon to good food, we've had some intense but good days with lots of new ideas 🎉.
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Yuki
Yuki@y_m_asano·
Now finally accepted at @emnlpmeeting! I think the technique and high-level ideas i) allow bidirectional attention for prompt & ii) (maybe) process input-query differently from answer generation will stick around.
Yuki@y_m_asano

Today we introduce Bidirectional Instruction Tuning (Bitune). It's a new way of adapting LLMs for the instruction->answering stage. It allows the model to process the instruction/question with bidirectional attention, while the answer generation remains causal.

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Yuki
Yuki@y_m_asano·
Today we release Franca, a new vision Foundation Model that matches and sometimes outperforms DINOv2. The data, the training code and the model weights (with intermediate checkpoints) are open-source, allowing everyone to build on this. Methodologically, we introduce two new SSL components, one is a multi-granularity SK clustering loss that utilizes Matryoshka representations and a quick post-pretraining scheme to remove unwanted spatial biases. This is the result of a close and fun collaboration @valeoai (in France) and @FunAILab (in Franconia)
Shashank@shawshank_v

Can open-data models beat DINOv2? Today we release Franca, a fully open-sourced vision foundation model. Franca with ViT-G backbone matches (and often beats) proprietary models like SigLIPv2, CLIP, DINOv2 on various benchmarks setting a new standard for open-source research🧵

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Ryousuke Yamada
Ryousuke Yamada@FragileGoodwill·
Hello FunAI Lab at UTN 👋 I’m excited to start a new chapter of my research journey here in Nuremberg as a visiting postdoc. Excited for inspiring collaborations and impactful research ahead with @y_m_asano and the amazing students😀
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Yuki
Yuki@y_m_asano·
LoRA et al. enable personalised model generation and serving, which is crucial as finetuned models still outperform general ones in many tasks. However, serving a base model with many LoRAs is very inefficient! Now, there's a better way: enter Prompt Generation Networks, presented today @BMVCconf
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Adina Yakup
Adina Yakup@AdinaYakup·
Is the community trying to surprise us today? 🤯 Because these benchmark-related papers from different research labs all dropped on the Daily Papers page at once! 🎉📑hf.co/papers ✨ LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models by Opendata lab ✨ MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models by @richardxp888 ✨ MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks by the TigerLab ✨ LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content by @TelAvivUni @IBMResearch ✨ Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models by @PKU1898 @AlibabaGroup ✨ TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models by @MuCai7 ✨ LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory by @DiWu0162 @TencentGlobal ✨ TVBench: Redesigning Video-Language Evaluation by @FunAILab @_akhaliq
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Yuki
Yuki@y_m_asano·
Today, we're introducing TVBench! 📹💬 Video-language evaluation is crucial, but are we doing it right? We find that current benchmarks fall short in testing temporal understanding. 🧵👇
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