1/1
CVPR 2024 Highlight Paper Alert
Paper Title: Matching Anything by Segmenting Anything
Few pointers from the paper
The robust association of the same objects across video frames in complex scenes is crucial for many applications, especially Multiple Object Tracking (MOT).
Current methods predominantly rely on labeled domain-specific video datasets, which limits the cross-domain generalization of learned similarity embeddings.
In this paper authors have proposed “MASA”, a novel method for robust instance association learning, capable of matching any objects within videos across diverse domains without tracking labels.
Leveraging the rich object segmentation from the Segment Anything Model (SAM), MASA learns instance-level correspondence through exhaustive data transformations.
They treat the SAM outputs as dense object region proposals and learn to match those regions from a vast image collection. Authors further designed a universal MASA adapter which can work in tandem with foundational segmentation or detection models and enable them to track any detected objects.
Those combinations present strong zero-shot tracking ability in complex domains. Extensive tests on multiple challenging MOT and MOTS benchmarks indicate that the proposed method, using only unlabeled static images, achieves even better performance than state-of-the-art methods trained with fully annotated in-domain video sequences, in zero-shot association
Organization: @ETH_en , @INSAITinstitute
Paper Authors: Siyuan Li, @leike_lk , @MDanelljan , Luigi Piccinelli, @MattiaSegu , Luc Van Gool, Fisher Yu
Read the Full Paper here: [2406.04221] Matching Anything by Segmenting Anything
Project Page: MASA
Code: GitHub - siyuanliii/masa: Official Implementation of CVPR24 highligt paper: Matching Anything by Segmenting Anything
Be sure to watch the attached Demo Video-Sound on
Find this Valuable ?
QT and teach your network something new
Follow me , @NaveenManwani17 , for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.
/search?q=#CVPR2024
To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196
CVPR 2024 Highlight Paper Alert
Paper Title: Matching Anything by Segmenting Anything
Few pointers from the paper
The robust association of the same objects across video frames in complex scenes is crucial for many applications, especially Multiple Object Tracking (MOT).
Current methods predominantly rely on labeled domain-specific video datasets, which limits the cross-domain generalization of learned similarity embeddings.
In this paper authors have proposed “MASA”, a novel method for robust instance association learning, capable of matching any objects within videos across diverse domains without tracking labels.
Leveraging the rich object segmentation from the Segment Anything Model (SAM), MASA learns instance-level correspondence through exhaustive data transformations.
They treat the SAM outputs as dense object region proposals and learn to match those regions from a vast image collection. Authors further designed a universal MASA adapter which can work in tandem with foundational segmentation or detection models and enable them to track any detected objects.
Those combinations present strong zero-shot tracking ability in complex domains. Extensive tests on multiple challenging MOT and MOTS benchmarks indicate that the proposed method, using only unlabeled static images, achieves even better performance than state-of-the-art methods trained with fully annotated in-domain video sequences, in zero-shot association
Organization: @ETH_en , @INSAITinstitute
Paper Authors: Siyuan Li, @leike_lk , @MDanelljan , Luigi Piccinelli, @MattiaSegu , Luc Van Gool, Fisher Yu
Read the Full Paper here: [2406.04221] Matching Anything by Segmenting Anything
Project Page: MASA
Code: GitHub - siyuanliii/masa: Official Implementation of CVPR24 highligt paper: Matching Anything by Segmenting Anything
Be sure to watch the attached Demo Video-Sound on
Find this Valuable ?
QT and teach your network something new
Follow me , @NaveenManwani17 , for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.
/search?q=#CVPR2024
To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196
A.I Generated explanation:
**Title:** Matching Anything by Segmenting Anything
**What's it about:** This paper is about a new way to track objects in videos, even if they're moving around or changing shape. This is important for things like self-driving cars, surveillance systems, and robots that need to follow objects.
**The problem:** Current methods for tracking objects in videos rely on labeled data, which means someone has to manually label each object in the video. This limits how well these methods work in different situations.
**The solution:** The authors of this paper have come up with a new method called "MASA" that can track objects in videos without needing labeled data. MASA uses a technique called "segmentation" to break down the video into smaller parts and then matches those parts across different frames.
**How it works:** MASA uses a powerful tool called the "Segment Anything Model" (SAM) to identify objects in the video. It then uses these objects to learn how to match them across different frames, even if they're moving or changing shape.
**The benefits:** MASA can track objects in videos without needing labeled data, which makes it more flexible and powerful than current methods. It can also work with different types of objects and in different situations.
**The results:** The authors tested MASA on several challenging video datasets and found that it performed better than current state-of-the-art methods, even though it didn't use labeled data.
**Who did it:** The paper was written by a team of researchers from ETH Zurich and the INSAIT Institute.
**Want to learn more:** You can read the full paper here: [2406.04221] Matching Anything by Segmenting Anything, visit the project page here: MASA, or check out the code on GitHub here: GitHub - siyuanliii/masa: Official Implementation of CVPR24 highligt paper: Matching Anything by Segmenting Anything.