Segmenting Transparent Object in the Wild with Transformer (2101.08461v3)

Abstract: This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application. (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version. Furthermore, a novel transformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly, the transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures. Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's transformer decoder, where each prototype learns the statistics of one category in the whole dataset. We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.

• The paper presents a transformer-based segmentation framework, Trans2Seg, that recasts segmentation as a dictionary lookup problem.
• It introduces the enhanced Trans10K-v2 dataset with 10,428 images and 11 detailed transparent object categories for real-world scenes.
• Trans2Seg achieves a mIoU of 72.1%, outperforming earlier CNN-based approaches and advancing robotic vision in handling transparent objects.

An Overview of "Segmenting Transparent Object in the Wild with Transformer"

The paper "Segmenting Transparent Object in the Wild with Transformer" introduces a robust solution to the challenge of segmenting transparent objects, which are pervasive yet problematic in applications such as robotic vision. This research is characterized by two main contributions: the establishment of a comprehensive dataset, Trans10K-v2, and the proposal of a novel transformer-based segmentation framework, Trans2Seg.

Trans10K-v2 Dataset

Trans10K-v2 extends the previously developed Trans10K dataset, offering significant advancements. It comprises 10,428 images annotated with 11 fine-grained categories of transparent objects frequently encountered in residential environments, such as glass doors, windows, bottles, and eyeglasses. This dataset not only provides greater diversity compared to previous datasets (characterized by limited images and categories) but also includes higher annotation quality with detailed masks, enhancing its utility for real-world applications.

The increased complexity and variety within Trans10K-v2 expose the limitations of existing segmentation methods while providing a challenging benchmark for advancing the field of semantic segmentation in segments involving transparent objects. It further includes scene annotations that detail the context in which these objects appear, enabling a deeper exploration of object-environment interactions and supporting more realistic robotic navigation and manipulation scenarios.

Trans2Seg: The Transformer-based Segmentation Architecture

The Trans2Seg model tackles the problem by leveraging the advantages of a transformer architecture over traditional CNNs, notably the ability to capture global contextual information due to its inherent self-attention mechanism. This model recasts semantic segmentation as a dictionary lookup problem where a set of learnable category prototypes serves as queries to the transformer's decoder. These prototypes dynamically fetch relevant features from the entire dataset, thus providing a flexible and context-aware approach to segmentation.

The authors demonstrate through empirical results that Trans2Seg remarkably exceeds the performance of CNN-based benchmarks on the newly introduced dataset, highlighting its superior capability in dealing with the intricate problems presented by transparent object segmentation. Notably, Trans2Seg achieves an mIoU score of 72.1%, significantly outperforming the previous best method, TransLab, which scored 69.0%.

Implications and Future Directions

The implications of this work extend into both theoretical and practical domains. Practically, the development of both the dataset and segmentation framework presents immediate advantages for robotic systems tasked with interacting within environments containing transparent objects. These advancements facilitate more reliable object recognition and manipulation, thus enhancing the autonomy and efficiency of robotic agents.

Theoretically, this research underscores the transformative potential of attention mechanisms within vision-related tasks, suggesting expanded roles for transformer architectures beyond traditional NLP applications. It opens avenues for future work to refine the use of transformers in segmentation tasks, potentially integrating with other vision-based systems or applied to additional complex real-world scenarios like autonomous driving.

In conclusion, the combination of the richly detailed Trans10K-v2 dataset and the innovative Trans2Seg model presents a significant step towards addressing the nuances of transparent object segmentation. The methodologies developed in this work invite extended research and application, paving the way for further integration of advanced AI techniques into complex vision-based tasks.