🚀 As #CVPR2025 week kicks off, meet SANSA: Semantically AligNed Segment Anything 2
We turn SAM2 into a semantic few-shot segmenter:
🧠 Unlocks latent semantics in frozen SAM2
✏️ Supports any prompt: fast and scalable annotation
📦 No extra encoders
📎 github.com/ClaudiaCutta...
We turn SAM2 into a semantic few-shot segmenter:
🧠 Unlocks latent semantics in frozen SAM2
✏️ Supports any prompt: fast and scalable annotation
📦 No extra encoders
📎 github.com/ClaudiaCutta...
June 2, 2025 at 6:08 PM
🚀 As #CVPR2025 week kicks off, meet SANSA: Semantically AligNed Segment Anything 2
We turn SAM2 into a semantic few-shot segmenter:
🧠 Unlocks latent semantics in frozen SAM2
✏️ Supports any prompt: fast and scalable annotation
📦 No extra encoders
📎 github.com/ClaudiaCutta...
We turn SAM2 into a semantic few-shot segmenter:
🧠 Unlocks latent semantics in frozen SAM2
✏️ Supports any prompt: fast and scalable annotation
📦 No extra encoders
📎 github.com/ClaudiaCutta...
Reposted by Gabriele Trivigno
Reposted by Gabriele Trivigno
To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
Davide Sferrazza, @berton-gabri.bsky.social Gabriele Trivigno, Carlo Masone
tl;dr: global descriptors nowadays are often better than local feature matching methods for simple datasets.
arxiv.org/abs/2504.06116
Davide Sferrazza, @berton-gabri.bsky.social Gabriele Trivigno, Carlo Masone
tl;dr: global descriptors nowadays are often better than local feature matching methods for simple datasets.
arxiv.org/abs/2504.06116
April 11, 2025 at 11:38 AM
To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
Davide Sferrazza, @berton-gabri.bsky.social Gabriele Trivigno, Carlo Masone
tl;dr: global descriptors nowadays are often better than local feature matching methods for simple datasets.
arxiv.org/abs/2504.06116
Davide Sferrazza, @berton-gabri.bsky.social Gabriele Trivigno, Carlo Masone
tl;dr: global descriptors nowadays are often better than local feature matching methods for simple datasets.
arxiv.org/abs/2504.06116
🔥 Our paper SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation is accepted as a #Highlight at #CVPR2025! 🎉
We make #SegmentAnything wiser, enabling it to understand textual prompts—training only 4.9M parameters! 🧠
💻 Code, models & demo: github.com/ClaudiaCutta...
Why SAMWISE?👇
We make #SegmentAnything wiser, enabling it to understand textual prompts—training only 4.9M parameters! 🧠
💻 Code, models & demo: github.com/ClaudiaCutta...
Why SAMWISE?👇
April 10, 2025 at 6:07 PM
🔥 Our paper SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation is accepted as a #Highlight at #CVPR2025! 🎉
We make #SegmentAnything wiser, enabling it to understand textual prompts—training only 4.9M parameters! 🧠
💻 Code, models & demo: github.com/ClaudiaCutta...
Why SAMWISE?👇
We make #SegmentAnything wiser, enabling it to understand textual prompts—training only 4.9M parameters! 🧠
💻 Code, models & demo: github.com/ClaudiaCutta...
Why SAMWISE?👇
Reposted by Gabriele Trivigno
To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
Davide Sferrazza, @berton-gabri.bsky.social, @gabtriv.bsky.social, Carlo Masone
tl;dr:VPR datasets saturate;re-ranking not good;image matching->uncertainty->inlier counts->confidence
arxiv.org/abs/2504.06116
Davide Sferrazza, @berton-gabri.bsky.social, @gabtriv.bsky.social, Carlo Masone
tl;dr:VPR datasets saturate;re-ranking not good;image matching->uncertainty->inlier counts->confidence
arxiv.org/abs/2504.06116
April 9, 2025 at 3:35 AM
To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
Davide Sferrazza, @berton-gabri.bsky.social, @gabtriv.bsky.social, Carlo Masone
tl;dr:VPR datasets saturate;re-ranking not good;image matching->uncertainty->inlier counts->confidence
arxiv.org/abs/2504.06116
Davide Sferrazza, @berton-gabri.bsky.social, @gabtriv.bsky.social, Carlo Masone
tl;dr:VPR datasets saturate;re-ranking not good;image matching->uncertainty->inlier counts->confidence
arxiv.org/abs/2504.06116
Reposted by Gabriele Trivigno
🚀 Paper Release! 🚀
Curious about image retrieval and contrastive learning? We present:
📄 "All You Need to Know About Training Image Retrieval Models"
🔍 The most comprehensive retrieval benchmark—thousands of experiments across 4 datasets, dozens of losses, batch sizes, LRs, data labeling, and more!
Curious about image retrieval and contrastive learning? We present:
📄 "All You Need to Know About Training Image Retrieval Models"
🔍 The most comprehensive retrieval benchmark—thousands of experiments across 4 datasets, dozens of losses, batch sizes, LRs, data labeling, and more!
March 18, 2025 at 10:41 PM
🚀 Paper Release! 🚀
Curious about image retrieval and contrastive learning? We present:
📄 "All You Need to Know About Training Image Retrieval Models"
🔍 The most comprehensive retrieval benchmark—thousands of experiments across 4 datasets, dozens of losses, batch sizes, LRs, data labeling, and more!
Curious about image retrieval and contrastive learning? We present:
📄 "All You Need to Know About Training Image Retrieval Models"
🔍 The most comprehensive retrieval benchmark—thousands of experiments across 4 datasets, dozens of losses, batch sizes, LRs, data labeling, and more!
Trying to convince my bluesky feed to put me in the Computer Vision community. Right now I only see posts about the orange-haired president. @berton-gabri.bsky.social @gabrigole.bsky.social how did you do it?
April 7, 2025 at 9:43 AM
Trying to convince my bluesky feed to put me in the Computer Vision community. Right now I only see posts about the orange-haired president. @berton-gabri.bsky.social @gabrigole.bsky.social how did you do it?
Reposted by Gabriele Trivigno
Image segmentation doesn’t have to be rocket science. 🚀
Why build a rocket engine full of bolted-on subsystems when one elegant unit does the job? 💡
That’s what we did for segmentation.
✅ Meet the Encoder-only Mask Transformer (EoMT): tue-mps.github.io/eomt (CVPR 2025)
(1/6)
Why build a rocket engine full of bolted-on subsystems when one elegant unit does the job? 💡
That’s what we did for segmentation.
✅ Meet the Encoder-only Mask Transformer (EoMT): tue-mps.github.io/eomt (CVPR 2025)
(1/6)
March 31, 2025 at 8:35 PM
Image segmentation doesn’t have to be rocket science. 🚀
Why build a rocket engine full of bolted-on subsystems when one elegant unit does the job? 💡
That’s what we did for segmentation.
✅ Meet the Encoder-only Mask Transformer (EoMT): tue-mps.github.io/eomt (CVPR 2025)
(1/6)
Why build a rocket engine full of bolted-on subsystems when one elegant unit does the job? 💡
That’s what we did for segmentation.
✅ Meet the Encoder-only Mask Transformer (EoMT): tue-mps.github.io/eomt (CVPR 2025)
(1/6)
