🚗🌐 Working on domain adaptation for 3D point clouds / LiDAR?
We'll present MuDDoS at BMVC: a method that boosts multimodal distillation for 3D semantic segmentation under domain shift.
📍 BMVC
🕚 Monday, Poster Session 1: Multimodal Learning (11:00–12:30)
📌 Hadfield Hall #859
The PhD graduation season in the team goes on!
Today, Corentin Sautier is defending his PhD on "Learning Actionable LiDAR Representations without Annotations".
Good luck! 🚀
It’s PhD graduation season in the team!
Today, @bjoernmichele.bsky.social is defending his PhD on "Domain Adaptation for 3D Data"
Best of luck! 🚀
The accounts/authors were probably fake. I couldn’t match them to real profiles. I think the goal was to increase the citation counts of certain papers while hiding it by citing many others. With a few exceptions, the same ~150 papers were cited each time, none from the authors.
Update: ResearchGate has investigated the case, and, as far as I can see, all the suspicious papers (~200) have now been removed. Many thanks to the @researchgate.bsky.social team!
If you're interested in human pose estimation and mesh recovery from LiDAR data, we have this massive survey: arxiv.org/abs/2509.12197
Salma and Nermin put a tremendous amount of work in it, there's everything: the tasks, all the methods organized, datasets, numbers, challenges and opportunities.
The issue was reported yesterday to ResearchGate. By the time I created the report, Google Scholar indexed about 30 other additional papers (uploaded by the same 4 accounts).
Curious to know if you’ve seen similar suspicious citations with your papers and what you did about it.
[4/]
But also:
(d) Reference lists are very similar across papers.
(f) All papers cover almost identical topics (segmentation/detection/recognition in medical imaging), far from the topic of our work.
(e) Our paper appears in the bibliography but I never saw it cited in the text.
[3/]
Some elements which raised suspicions:
(a) Papers uploaded from 4 accounts only.
(b) At least 1 paper claims a publication date in 2021, before RangeViT, and cites papers from 2025.
(c) Rate of uploads is accelerating: from 1 paper on August 20 to 19 papers on September 9.
[2/]
Discovered that our RangeViT paper keeps being cited in what might be LLM-generated papers. Number of citations increased rapidly in the last weeks. Too good to be true.
Papers popped up on different platforms, but mainly on ResearchGate with ~80 papers in just 3 weeks.
[1/]
1/ 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.
We just released the code of #LiDPM, go ahead and play with it (and don't forget to star 🤭🤩)!
Training and inference code available, along with the model checkpoint.
Github repo: github.com/astra-vision...
#IV2025
1/n 🚀New paper out - accepted at #ICCV2025!
Introducing DIP: unsupervised post-training that enhances dense features in pretrained ViTs for dense in-context scene understanding
Below: Low-shot in-context semantic segmentation examples. DIP features outperform DINOv2!
Presenting our project #LiDPM in the afternoon oral session at #IV2025!
Project page: astra-vision.github.io/LiDPM/
w/ @gillespuy.bsky.social, @alexandreboulch.bsky.social, Renaud Marlet, Raoul de Charette
Also, see our poster at 3pm in the Caravaggio room and AMA 😉
🚨 New preprint!
How far can we go with ImageNet for Text-to-Image generation? w. @arrijitghosh.bsky.social @lucasdegeorge.bsky.social @nicolasdufour.bsky.social @vickykalogeiton.bsky.social
TL;DR: Train a text-to-image model using 1000 less data in 200 GPU hrs!
📜https://arxiv.org/abs/2502.21318
🧵👇
We @imagineenpc.bsky.social are slowly but surely entering our proposals for master's degree internships here: docs.google.com/document/d/1...
These are 6 months projects that typically correspond to the end-of-study project in the French curriculum.
Probably more offers to come, check it regularly.