🚨CVPR 2025 Highlight Paper Alert 🚨
➡️Paper Title: SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation
🌟Few pointers from the paper
🎯Referring Video Object Segmentation (RVOS) relies on natural language expressions to segment an object in a video clip.
🎯Existing methods restrict reasoning either to independent short clips, losing global context, or process the entire video offline, impairing their application in a streaming fashion.
🎯In this work, authors aimed to surpass these limitations and design an RVOS method capable of effectively operating in streaming-like scenarios while retaining contextual information from past frames.
🎯They build upon the Segment-Anything 2 (SAM2) model, that provides robust segmentation and tracking capabilities and is naturally suited for streaming processing.
🎯They made SAM2 wiser, by empowering it with natural language understanding and explicit temporal modeling at the feature extraction stage, without fine-tuning its weights, and without outsourcing modality interaction to external models.
🎯To this end, they introduced a novel adapter module that injects temporal information and multi-modal cues in the feature extraction process.
🎯They further revealed the phenomenon of tracking bias in SAM2 and proposed a learnable module to adjust its tracking focus when the current frame features suggest a new object more aligned with the caption.
🎯Their proposed method, “SAMWISE”, achieves state-of-the-art across various benchmarks, by adding a negligible overhead of less than 5 M parameters.
🏢Organization: Politecnico di Torino [
@PoliTOnews ],
@FocoosAI
🧙Paper Authors: Claudia Cuttano,
@gabTrivv , Gabriele Rosi,
@masone_carlo , Giuseppe Averta
📝 Read the Full Paper here:
arxiv.org/abs/2411.17646
🗂️ Project Page:
claudiacuttano.github.io/SAM…
🧑💻 Code:
github.com/ClaudiaCuttano/SA…
🎥 Be sure to watch the attached Demo Video - Sound on 🔊🔊
🎵 Music by Adi Iswanto from
@pixabay
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