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flomgr.bsky.social
She/her Computer vision researcher @evs & PhD student @universitedeliege.bsky.social
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Where are the women in AI? www.interface-eu.org/publications...

The serious research we all need: arxiv.org/abs/2501.00536

Let us demonstrate some meaningful improvements on the 186 images whose GT is already questioned by [19]. Let's also pretend [27]'s dataset does not exist. Hence our work, inspired by [14] can only be compared with it on synthetic data.

Our newest SoccerNet challenge on Monocular Depth Estimation is up and running! πŸ₯³ Our world is 3D, football games are too, do you have what it takes to lift football analysis from 2D to 3D? 🏟️⚽️

I wish I stumbled on this pretty and clear intro to CUDA and GPUs years ago : modal.com/gpu-glossary

2βƒ£πŸš€ #SoccerNet2025 Let's present our Challenge #2: β€œGame State Reconstruction” 🏟 Reconstruct a schematic state of the game over time, capturing key player positions and overall game dynamics! πŸ”— soccer-net.org/challenges/2... #AIChallenge #SportsAnalytics @cvprconference.bsky.social

At #SIGGRAPHAsia, I just saw a paper talk where many of the examples shown are about making videos of women in short skirts and gym clothes dance to whatever sequence you want from a single image. How does this get published?

Most SISR methods lately: "This new method boasts a jaw-dropping 0.02 PSNR gain and was validated on, wait for it, 19 images. Truly the stuff of legends. πŸ˜‚ #ScienceIsAwesome #InnovateOrDie"

Our paper "πŸ‘πƒ π‚π¨π§π―πžπ± 𝐒𝐩π₯𝐚𝐭𝐭𝐒𝐧𝐠: π‘πšππ’πšπ§πœπž π…π’πžπ₯𝐝 π‘πžπ§ππžπ«π’π§π  𝐰𝐒𝐭𝐑 πŸ‘πƒ 𝐒𝐦𝐨𝐨𝐭𝐑 π‚π¨π§π―πžπ±πžπ¬β€ is now available on Arxiv! πŸ’»Project page for some cool visualizations: convexsplatting.github.io πŸ““ Paper: arxiv.org/abs/2411.14974

Check out this BEAUTIFUL interactive blog about cameras and lenses ciechanow.ski/cameras-and-...

πŸš€ 🌟 #SoccerNet Challenges 2025 are here! In this 5th edition, we’re excited to unveil four diverse tasks! ⚽️ Check the deadline, there’s plenty of time to craft a groundbreaking solution and rise to the top of the leaderboard! πŸ† πŸ”₯Let the Challenges begin! #CVPR2025

SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction Yutao Tang, Yuxiang Guo, Deming Li, Cheng Peng tl;dr: align Mast3r to colmap point cloud, align outliers separately, as per semantic group -> splat arxiv.org/abs/2411.12592

DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes Hao Li, et al. tl;dr: scene->non-overlap regions->sparse images->Splatt3R+global alignment+depth priors->initialization->local GS models->distillation-based model aggregation arxiv.org/pdf/2411.12309

Future Bsky homes for our #computervision conferences! @cvprconference.bsky.social @iccv.bsky.social @eccv.bsky.social @wacvconference.bsky.social Stay tuned!

Image Matching Filtering and Refinement by Planes and Beyond Fabio Bellavia, Zhenjun Zhao, Luca Morelli, Fabio Remondino tl;dr: refine matches by estimating local homographies with special parameterization MiHo and NCC. Improves even LG/LoFTR. Many additional analyses. arxiv.org/abs/2411.09484

MFTIQ: Multi-Flow Tracker with Independent Matching Quality Estimation Jonas Serych, Michal Neoral, Jiri Matas tl;dr: algorithm for robust chaining optical flow in video + learned quality estimation module. RoMA rules, SeaRAFT is second best. arxiv.org/abs/2411.09551