📄 Read the full paper:
SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation
Now on arXiv → arxiv.org/abs/2505.21795
🔹 4/4 – Promptable segmentation in action SANSA reduces reliance on costly pixel-level masks by supporting point, box, and scribble prompts
📈enabling fast, scalable annotation with minimal supervision.
See the qualitative results 👇
🔹 3/4 – SANSA achieves state-of-the-art in few-shot segmentation. We outperform specialist and foundation-based methods across various benchmarks:
📈 +9.3% mIoU on LVIS-92i
⚡ 3× faster than prior works
💡 Only 234M parameters (4-5x smaller than competitors)
🔹2/4 – Unlocking semantic structure
SAM2 features are rich, but optimized for tracking.
🧠 Insert bottleneck adapters into frozen SAM2
📉 These restructure feature space to disentangle semantics
📈 Result: features cluster semantically—even for unseen classes (see PCA👇)
🚀 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...
#ICCV2025
I guess merging the events could also work 😂 I wonder whether cricket players would be better at ComputerVision than CV researchers are at cricket, or viceversa
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
✨ SAMWISE achieves state-of-the-art performance across multiple #RVOS benchmarks—while being the smallest model in RVOS! 🎯 It also sets a new #SOTA in image-level referring #segmentation. With only 4.9M trainable parameters, it runs #online and requires no fine-tuning of SAM2 🚀
🚀 Contributions:
🔹 Textual Prompts for SAM2: Early fusion of visual-text cues via a novel adapter
🔹 Temporal Modeling: Essential for video understanding, beyond frame-by-frame object tracking
🔹 Tracking Bias: Correcting tracking bias in SAM2 for text-aligned object discovery
🔥 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?👇
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
🚀 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!
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?
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)
Went outside today and thought this would be perfect for my first #bluesky post
