Oriane Siméoni
@oriane_simeoni
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RT @p_bojanowski: 🔥Our team working on unsupervised deep learning is looking for a PhD intern for 2025. We are looking for deep learning /…
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@ducha_aiki @abursuc Thanks for catching my best expression. Things are serious when you talk about CLIP-DINOiser!
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RT @abursuc: Our, now annual, Weakly Supervised Computer Vision workshop @DeepIndaba turned out great! Thanks to the participants, amazing…
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The link to the workshop: which will start at 8:30 🕗
Excited to be at @DeepIndaba in Dakar and to take part tomorrow in the 3rd Weakly Sup. Computer Vision workshop organized by R. de Charette & co. 💬 I will give a talk about one of my favorite subjects: using the power of self-sup. features for object localization. Come say hi!
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Excited to be at @DeepIndaba in Dakar and to take part tomorrow in the 3rd Weakly Sup. Computer Vision workshop organized by R. de Charette & co. 💬 I will give a talk about one of my favorite subjects: using the power of self-sup. features for object localization. Come say hi!
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How to to obtain high-quality 3D representation from 2D self-supervised backbones? #CVPR2024 Check-out ScaLR which narrows down from 30 to 10pts the difference to fully-supervised models with our homemade WaffleIron model Both works led by @gillespuy👏.
📢We introduce the ScaLR models (code+checkpoints) for LiDAR perception distilled from vision foundation models tl;dr: don’t neglect the choice of teacher, student, and pretraining datasets -> their impact is probably more important than the distillation method #CVPR2024 🧵 [1/8]
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Last December @AVobecky presented our POP-3D model which generates 3D occupancy with open-vocabulary representation from images only, trained w/o annotation. We also proposed a new small benchmark for open-voc. 3D-occupancy w/ natural language queries. More details below ⬇️
[@NeurIPSConf'23]🚨Did you miss it? Our POP-3D generates open-vocabulary 3D occupancy predictions from 📷 surround-view images only, w/o human labels & w/ distillation from pre-trained models. We also propose a new small 3D-occupancy open-vocabulary benchmark #neurips2023⬇️ [1/N]
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RT @EloiZablocki: Are motion forecasting models ready for deployment? 🤔 In this new #ICRA2024 paper, we reveal several challenges that for…
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We strengthen MaskCLIP features w. a simple weighted aggregation strategy w. weights learnt using self-sup. DINO🦖 ➡️patch-level CLIP abilities 🧪SoTA in open-voc sem. seg. 🚀pass in CLIP & new conv 3x3 Check out CLIP-DINOiser 📜 🖥️
🚨Happy to release on arXiv CLIP-DINOiser: Teaching CLIP a few DINO tricks🦖🎓 We obtain dense CLIP features in 1 forward pass w/o feature alteration and w/ almost no computational extra cost to facilitate open-vocabulary semantic segmentation 🧶 🖥️: [1/N]
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RT @Mickael_Chen: We are presenting our poster on links between GANs and Diffusion at Neurips. Don't be scared by the formalism, @jy_franc…
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🍾POP-3D @NeurIPSConf If you want to discuss open-vocabulary 3D occupancy prediction from images only, reach out to @AVobecky who will present the poster on Thursday: Poster #115 @🗺️Great Hall & Hall B1+B2 (level 1) ⏲️Th. 14 Dec. 10:45 a.m. CST — 12:45 p.m. CST
@VLetzelter @MathieuFontai19 @Mickael_Chen POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images by @AVobecky @oriane_simeoni D.Hurych @SpyrosGidaris @abursuc P.Pérez, J.Sivic tl;dr: open-vocab 3D semantic occupancy maps from multi-cam inputs w/ tri-modal self-supervised learning #neurips2023
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RT @valeoai: @VLetzelter @MathieuFontai19 @Mickael_Chen POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images by @AVobecky @oriane_si…
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Thank you @matas_jiri & @giotolias for the invitation! It was a great day with very insightful talks & discussions with VRG students/researchers. Prague is still just as welcoming even under the snow ❄️
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