Matching Anything by Segmenting Anything (2406.04221v1)

Abstract: 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. We propose 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. We treat the SAM outputs as dense object region proposals and learn to match those regions from a vast image collection. We further design 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. Project Page: https://matchinganything.github.io/

Matching Anything by Segmenting Anything

The paper "Matching Anything by Segmenting Anything" proposes a novel method named MASA (Matching Anything by Segmenting Anything), aimed at enhancing instance association learning in the domain of Multiple Object Tracking (MOT). Specifically, the approach leverages the Segment Anything Model (SAM) to overcome the limitations posed by domain-specific labeled video datasets in existing methods.

The MASA pipeline uses SAM's robust object segmentation capability to generate dense object region proposals, from which instance-level correspondences are learned through diverse data augmentations. This framework addresses two principal challenges: (1) acquiring matching supervision for general objects across diverse domains without substantial labeling costs, and (2) integrating this generalizable tracking capability with existing segmentation and detection models to enable tracking of any detected object.

Methodology

MASA Pipeline

The MASA pipeline's methodology revolves around using SAM to generate exhaustive instance masks automatically. The process begins with applying strong data transformations to an unlabeled image, resulting in different views with pixel correspondences. By leveraging SAM's segmentation outputs, pixel-level correspondence is transformed into dense instance-level correspondences. These correspondences serve as self-supervision signals, enabling the use of contrastive learning techniques to learn discriminative object representations.

Universal MASA Adapter

The MASA adapter is designed to work in conjunction with foundational segmentation and detection models to enhance their tracking capabilities. It maintains the original detection and segmentation faculties of these models by freezing their backbones and adding the MASA adapter on top. This adapter incorporates dynamic feature fusion and a multi-scale feature pyramid for efficient feature integration across spatial locations and feature levels. Moreover, a detection head is utilized during training to distill SAM's detection knowledge into the MASA adapter, significantly accelerating SAM's everything mode for tracking applications.

Inference

The MASA framework can operate in different modes:

1. Detect and Track Anything: Using detection observations, MASA extracts tracking features and applies bi-softmax nearest neighbor search for instance matching.
2. Segment and Track Anything: SAM’s detected boxes are used to prompt both the SAM mask decoder and the MASA adapter.
3. Testing with Given Observations: External detection observations are employed to prompt feature extraction through the MASA adapter.

Experimental Results

Extensive experiments demonstrate MASA's efficacy across various challenging MOT/MOTS benchmarks:

1. TAO TETA Benchmark: The MASA variants, especially those based on Grounding-DINO and Detic, outperform state-of-the-art (SOTA) methods, even in zero-shot association settings.
2. BDD MOTS and MOT: MASA achieves the highest association accuracy (AssocA and mIDF1 scores) compared to other SOTA trackers, indicating the robustness of its learned instance embeddings.
3. UVO Video Segmentation: MASA’s zero-shot open-world tracking performance surpasses existing methods, highlighting its capability to manage diverse and complex environments.

Implications and Future Directions

The implications of MASA’s methodology are manifold:

• Practical Applications: MASA's ability to generalize tracking across various domains without requiring specific annotations suggests its significant potential for applications in autonomous driving, surveillance, and robotics.
• Model Adaptability: The MASA adapter's design underscores the potential to enhance existing detection and segmentation models with robust tracking capabilities using only static image data.
• Efficiency and Speed: The distillation approach embedded in the MASA adapter not only improves tracking accuracy but also expedites segmentation processes.

Future Developments

Several future advancements can further enhance MASA's capabilities:

1. Consistency in Proposal Generation: Improving the temporal consistency of detection or segmentation results across video frames to mitigate the flickering effect.
2. Long-term Occlusion Handling: Developing a more sophisticated long-term memory system to better manage occluded objects.
3. Broader Domain Adaptation: Extending MASA's domain adaptation efforts to handle more varied environmental conditions and object types.

Conclusion

The MASA framework delineates a robust and adaptable approach to instance association learning through the integration of SAM-derived segmentation outputs. Its ability to match and track any object across diverse domains without relying on domain-specific annotated videos represents a significant advancement in the field of multiple object tracking. This paper sets the groundwork for future explorations into more efficient and generalizable tracking methodologies in computer vision.