Wei Lin @ CVPR 2025

@WeiLinCV

Research associate @ ELLIS Unit, LIT AI Lab, Institute for Machine Learning, JKU Linz. Collab with MIT-IBM Watson AI Lab. PhD@TU Graz

Graz, Austria

wlin-at.github.io

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Wei Lin @ CVPR 2025
@WeiLinCV
Jan 27
Excited to share our paper “PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal Inconsistencies” has been accepted to ICLR 2026 🎉 🥳This work means a lot to me as it's my first time serving as the last author supervising a Master student Huge congrats to Lukas Selch!
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Wei Lin @ CVPR 2025
@WeiLinCV
Sep 8, 2025
🚀 🚀 We are introducing VisualOverload🎨🖼️, a VQA benchmark designed to test fundamental vision skills in visually dense scenes. 2,720 Q&A pairs across 6 tasks, 150 high-res artworks, and private ground truth. Even top VLMs hit only ~20% on the hardest tasks. Try it yourself🤖👉
Paul Gavrikov
@PaulGavrikov
Sep 8, 2025
Is basic image understanding solved in today’s SOTA VLMs? Not quite. We present VisualOverload, a VQA benchmark testing simple vision skills (like counting & OCR) in dense scenes. Even the best model (o3) only scores 19.8% on our hardest split.
Wei Lin @ CVPR 2025
@WeiLinCV
Jun 25, 2025
🚨 New @ICCVConference 2025 paper! Can GPT-4o actually localize an object from just a few examples? Turns out not really. In our @ICCVConference paper, we propose a simple fix: teach it from video tracking data. Results? Better few-shot localization, stronger context grounding.
Sivan Doveh
@SivanDoveh
Jun 25, 2025
IPLOC accepted to ICCV25 ☺️ Thanks to all the people that were part of it 🩷 The idea for this paper came by a lake during a visit to Graz for a talk. It has traveled with me through too many countries and too many wars, and it’s now a complete piece of work.
Wei Lin @ CVPR 2025
@WeiLinCV
Jun 16, 2025
Check our new work pLSTM that brings the power of linear RNNs to arbitrary DAGs and multi-dimensional data, enabling parallel computation and long-range modeling. It outperforms Transformers on extrapolation tasks and handles images, graphs, and grids with remarkable efficiency.
Korbinian Poeppel
@KorbiPoeppel
Jun 16, 2025
Ever wondered how linear RNNs like #mLSTM (#xLSTM) or #Mamba can be extended to multiple dimensions? Check out "pLSTM: parallelizable Linear Source Transition Mark networks". #pLSTM works on sequences, images, (directed acyclic) graphs. Paper link: arxiv.org/abs/2506.11997
Wei Lin @ CVPR 2025
@WeiLinCV
Jun 14, 2025
Check out our poster and talk with Guofeng at #355 in ExHall D. PerLA, is our new 3D language assistant that helps LLMs better understand the physical world! PerLA fuses local details and global context from point clouds using cross-attention + GNNs, and achieves SOTA on 3D bench
Guofeng(Ryan) Mei and 3 others
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