🔹 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 👇

🔹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...

🚀 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?👇

Went outside today and thought this would be perfect for my first #bluesky post