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Semantic-SAM: Segment and Recognize Anything at Any Granularity (2307.04767v1)

Published 10 Jul 2023 in cs.CV

Abstract: In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, we consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. This allows our model to capture rich semantic information. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks. Notably, this work represents the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves semantic-awareness and granularity-abundance. Furthermore, combining SA-1B training with other segmentation tasks, such as panoptic and part segmentation, leads to performance improvements. We will provide code and a demo for further exploration and evaluation.

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Semantic-SAM: Segment and Recognize Anything at Any Granularity

The paper "Semantic-SAM: Segment and Recognize Anything at Any Granularity" presents a universal model for image segmentation aimed at versatile and comprehensive recognition capabilities. The authors introduce Semantic-SAM, a model designed to recognize semantic features across various levels of granularity within images, thereby addressing multiple segmentation tasks simultaneously. This solution leverages a combination of existing datasets, multi-choice learning techniques, and advanced model architectures to enhance image segmentation efficacy.

Model Architecture and Training Paradigm

The Semantic-SAM model introduces a flexible architecture by employing a query-based mask decoder, similar to methods in the Mask DINO framework. This design allows it to handle varied inputs such as points and bounding boxes, thus supporting a multitude of segmentation scenarios. Notably, this is achieved through multi-choice learning and a many-to-many matching strategy, which enable the model to produce segmented outputs at different granularity levels from a single input. Unlike traditional single-output pipelines that limit granularity prediction, this architecture enhances the model’s ability to discern and delineate intricate object-part relationships.

Training Semantic-SAM involves integrating multiple datasets that provide annotations at different semantic and granularity levels. The training process is strategically structured to foster semantic awareness and granularity abundance, incorporating data from well-known datasets such as MSCOCO, ADE20k, and newly developed resources like SA-1B. By fusing object-level and part-level datasets with interactive segmentation datasets, the training approach not only enriches semantic richness but also improves the model’s adaptability to diverse visual environments. Decoupled classification techniques further refine the model’s capacity for detecting and classifying objects and parts distinctly, an approach that facilitates detailed semantic understanding across varied segmentation tasks.

Experimental Validation

Experimental evaluation on datasets such as COCO Val2017 indicates marked improvements in segmentation performance. The Semantic-SAM model, when assessed alongside previous models like Mask2Former and OpenSeed, demonstrated enhanced performance, particularly when trained jointly on segmentation-specific datasets along with SA-1B. Noteworthily, the model's performance gains were more pronounced on tasks involving smaller objects, reflecting the model's effectiveness at capturing finer granularity details.

In the context of multi-granularity interactive segmentation, Semantic-SAM outperformed existing frameworks by producing higher quality masks with more diverse granularity levels. The novel many-to-many matching strategy used in training crucially contributed to this performance, allowing the model to effectively manage the ambiguity associated with varied semantic granularity.

Practical and Theoretical Implications

Semantic-SAM represents a distinct step towards developing universal segmentation models capable of addressing a wide spectrum of segmentation tasks without sacrificing granularity or semantic detail. This advancement is significant for practical applications in areas such as autonomous systems, medical imaging, and any domain requiring detailed object-part recognition and segmentation.

Theoretically, this work underscores the potential of multi-choice learning schemes and data unification strategies in developing robust segmentation models. The approach could be extended to other vision tasks, potentially advancing models which require an overview of multiple data inputs and varied classification tasks.

Future Trajectories

As models like Semantic-SAM continue to evolve, future research might explore the integration of additional real-world datasets to improve model generalization and robustness in diverse environments. Advances in interactive segmentation techniques, incorporating user feedback and dynamic input adaptations, could also further refine model output precision.

The potential symbiosis with other emerging technologies, such as vision-LLMs, presents another frontier. Incorporating semantic understanding that integrates textual descriptions alongside visual data could usher in a new era of segmentation models capable of operating in more complex semantic spaces.

Overall, Semantic-SAM stands as a testament to the capacity for blending advanced architectural strategies with expansive training datasets to address the multifaceted challenges of semantic image segmentation.

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Authors (9)
  1. Feng Li (286 papers)
  2. Hao Zhang (948 papers)
  3. Peize Sun (33 papers)
  4. Xueyan Zou (21 papers)
  5. Shilong Liu (60 papers)
  6. Jianwei Yang (93 papers)
  7. Chunyuan Li (122 papers)
  8. Lei Zhang (1689 papers)
  9. Jianfeng Gao (344 papers)
Citations (138)
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