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Segment Everything Everywhere All at Once (2304.06718v4)

Published 13 Apr 2023 in cs.CV
Segment Everything Everywhere All at Once

Abstract: In this work, we present SEEM, a promptable and interactive model for segmenting everything everywhere all at once in an image, as shown in Fig.1. In SEEM, we propose a novel decoding mechanism that enables diverse prompting for all types of segmentation tasks, aiming at a universal segmentation interface that behaves like LLMs. More specifically, SEEM is designed with four desiderata: i) Versatility. We introduce a new visual prompt to unify different spatial queries including points, boxes, scribbles and masks, which can further generalize to a different referring image; ii) Compositionality. We learn a joint visual-semantic space between text and visual prompts, which facilitates the dynamic composition of two prompt types required for various segmentation tasks; iii) Interactivity. We further incorporate learnable memory prompts into the decoder to retain segmentation history through mask-guided cross-attention from decoder to image features; and iv) Semantic-awareness. We use a text encoder to encode text queries and mask labels into the same semantic space for open-vocabulary segmentation. We conduct a comprehensive empirical study to validate the effectiveness of SEEM across diverse segmentation tasks. Notably, our single SEEM model achieves competitive performance across interactive segmentation, generic segmentation, referring segmentation, and video object segmentation on 9 datasets with minimum 1/100 supervision. Furthermore, SEEM showcases a remarkable capacity for generalization to novel prompts or their combinations, rendering it a readily universal image segmentation interface.

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Analyzing SEEM: A Comprehensive Approach to Image Segmentation

The paper "Segment Everything Everywhere All at Once" introduces SEEM, a model for image segmentation tasks that provides a unified and interactive interface. Focused on the segmentation challenge, SEEM addresses various segmentation needs—semantic, instance, and panoptic segmentation—within an open-set framework. The authors emphasize SEEM’s versatility, compositionality, interactivity, and semantic-awareness, drawing analogies between its universal interface potential to the capabilities exhibited by LLMs.

Technical Methodology and Key Design Elements

SEEM is structured around a distinctive decoder mechanism enabling diverse prompting, similar to how advanced LLMs function in text processing. The model uses visual prompts to unify different spatial queries—like points, boxes, and masks—placing them within a joint visual-semantic space. This integration allows for dynamic compositions between visual and textual prompts, paving the way for SEEM’s strong compositionality capability. By incorporating memory prompts, SEEM retains previous segmentation information, enhancing its interactivity. A text encoder further enriches SEEM’s function by encoding text queries and mask labels in the same space for open-vocabulary segmentation tasks. Thus, SEEM bridges the gap to a universal segmentation model.

Empirical Validation

Empirical studies validate SEEM's performance across several datasets. The model demonstrates strong results in interactive segmentation, generalized segmentation tasks, and video object segmentation using minimal supervision. For example, SEEM exhibits competitive performance across nine diverse datasets. Importantly, it achieves this with only 1/100th of the amount of labeled data typically required. This efficiency underscores the model's ability to generalize and adapt to novel prompts, such as using an exemplar image segment for additional insight into unseen tasks.

Implications for Image Segmentation

The introduction of SEEM marks a potential shift in the development of universal models for image segmentation. Its capabilities highlight advancements in the automatic alignment between visual input and textual data, which can significantly reduce the manual labor needed for dataset labeling. Furthermore, SEEM’s composability allows practitioners to interactively refine tasks and adapt segmentation tasks without retraining or significant modifications on the model.

Future Prospects in AI

Looking forward, SEEM’s approach could inspire advancements in developing more generalized models that accommodate multi-modal inputs. Given its promising results, future research could extend SEEM’s framework beyond image segmentation to other domains where interactive and multi-modal data handling is essential. The continued evolution of computational power and more sophisticated training datasets will likely support this trajectory, leading to more sophisticated and robust AI models.

In conclusion, SEEM’s contribution to the field of image segmentation is a model designed for adaptability and universality—a testament to the potential of prompt-based architectures in image processing. While SEEM has set a high standard for future segmentation interfaces, its implications underscore the broader movement towards more interactive and universal AI models capable of complex task handling.

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Authors (9)
  1. Xueyan Zou (21 papers)
  2. Jianwei Yang (93 papers)
  3. Hao Zhang (947 papers)
  4. Feng Li (286 papers)
  5. Linjie Li (89 papers)
  6. Jianfeng Wang (149 papers)
  7. Lijuan Wang (133 papers)
  8. Jianfeng Gao (344 papers)
  9. Yong Jae Lee (88 papers)
Citations (347)
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