Sketch Input Method Editor: A Comprehensive Dataset and Efficient Methodology for Systematic Sketch Recognition and Segmentation

Core Concepts

The core message of this article is to present a comprehensive dataset of sketches from a professional C4I system and propose a simultaneous recognition and segmentation network with multilevel supervision, which enhances the interpretability and practicality of the system. The network is further equipped with few-shot domain adaptation and class-incremental learning to improve its adaptability to new users and extendibility to new task-specific sketches.

Abstract

The article presents the SketchIME dataset, which is the first large-scale and systematic dataset of sketches from a professional C4I system, containing 374 categories and 139 semantic components. The authors propose a simultaneous recognition and segmentation network, SketchRecSeg, which uses a two-stream architecture with CNN and GNN components. The recognition stream provides multilevel supervision to the segmentation stream, enhancing the interpretability of the network. To improve the practicality of SketchIME, the authors incorporate few-shot domain adaptation techniques to enhance the network's adaptability to new users' sketching styles. They also explore few-shot class-incremental learning to improve the network's extendibility to new task-specific sketch categories and semantic components. Experiments on the SketchIME dataset and the SPG dataset demonstrate the superior performance of the proposed SketchRecSeg network compared to state-of-the-art methods in both recognition and segmentation tasks. The analysis shows that the simultaneous recognition and segmentation, as well as the multilevel supervision, can benefit both tasks. The few-shot domain adaptation and class-incremental learning further enhance the network's adaptability and extendibility, making it more practical for real-world applications.

Stats

The SketchIME dataset contains 56,474 sketches from 374 categories and 139 semantic components, sketched by 18 participants. The SketchIME-SRS dataset contains 56,474 sketches for training and 19,781 sketches for testing. The SketchIME-CIL1 dataset contains 20,904 sketches in the base session and 800 sketches in the incremental sessions. The SketchIME-CIL2 dataset contains 21,077 sketches in the base session and 800 sketches in the incremental sessions.

Quotes

"Sketch Input Method Editor (SketchIME) specifically designed for a professional C4I system." "Our proposed simultaneous recognition and segmentation architecture, with the recognition stream providing supervision to the segmentation stream based on prior knowledge." "The incorporation of few-shot domain adaptation and class-incremental learning enhances the network's practicality by improving its adaptability to new users and extendibility to new task-specific classes."

Key Insights Distilled From

Sketch Input Method Editor

by Guangming Zh... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2311.18254.pdf

Deeper Inquiries

How can the unused data containing different stroke orders be utilized for online sketch recognition and personal identification based on sketching styles?

The unused data containing different stroke orders can be utilized for online sketch recognition and personal identification based on sketching styles by implementing a stroke order normalization technique. This technique involves preprocessing the sketches to align the stroke orders to a standardized format before feeding them into the recognition system. By normalizing the stroke orders, the system can learn to recognize the underlying patterns and features of the sketches regardless of the order in which the strokes were drawn. This approach can help improve the robustness and accuracy of the recognition system, enabling it to identify users based on their unique sketching styles.

How can the SketchIME system be integrated with other modalities, such as speech or gesture, to provide a more comprehensive and natural input experience for professional C4I systems?

The integration of the SketchIME system with other modalities, such as speech or gesture, can enhance the input experience for professional C4I systems by providing users with a more comprehensive and natural interaction interface. Here are some ways in which this integration can be achieved: Multimodal Input Fusion: By combining sketch input with speech and gesture inputs, users can interact with the system using a combination of modalities. This fusion can provide more context-rich input and improve the overall user experience. Contextual Understanding: The system can leverage speech recognition to interpret verbal commands or annotations related to the sketches. Gesture inputs can be used for spatial interactions or to manipulate the sketches on the interface. Adaptive User Profiles: By integrating speech and gesture inputs, the system can create adaptive user profiles that capture individual preferences and interaction styles. This personalized approach can enhance user experience and efficiency. Real-time Collaboration: The integration of multiple modalities can facilitate real-time collaboration among users, allowing them to communicate and work together more effectively on creating and editing sketches for C4I systems. Overall, integrating SketchIME with speech and gesture modalities can create a more intuitive and efficient input system for professional C4I applications, enhancing user productivity and collaboration.

How can the SketchIME system be integrated with other modalities, such as speech or gesture, to provide a more comprehensive and natural input experience for professional C4I systems?

To further enhance the adaptability and extendibility of the SketchIME system beyond domain adaptation and class-incremental learning, the following techniques can be explored: Transfer Learning: Utilize transfer learning techniques to leverage pre-trained models on large datasets to improve the performance of SketchIME on new users or tasks. Fine-tuning the pre-trained models on specific sketching styles or categories can enhance adaptability. Meta-Learning: Implement meta-learning algorithms to enable the system to quickly adapt to new tasks or users with limited data. Meta-learning can help the system generalize from past experiences and make rapid adjustments for new scenarios. Reinforcement Learning: Incorporate reinforcement learning to enable the SketchIME system to learn and adapt through interactions with users. By rewarding the system for accurate recognition and segmentation, it can continuously improve its performance over time. Generative Adversarial Networks (GANs): Explore the use of GANs to generate synthetic data for training the SketchIME system. By creating additional training samples that mimic different sketching styles or categories, the system can improve its adaptability to diverse inputs. By integrating these advanced techniques alongside domain adaptation and class-incremental learning, the SketchIME system can further enhance its adaptability and extendibility, making it more robust and versatile for professional C4I applications.