📄 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👇)

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

✨ 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