Spatial-Temporal Part-aware Network for Isolated Sign Language Recognition

Khái niệm cốt lõi

A new Spatial-Temporal Part-aware Network (StepNet) that effectively captures the fine-grained spatial and temporal cues in sign language videos without using any keypoint-level annotations.

Tóm tắt

The article proposes a new framework called Spatial-Temporal Part-aware Network (StepNet) for isolated sign language recognition. StepNet consists of two key modules:
Part-level Spatial Modeling:

Automatically captures the appearance-based properties, such as hands and faces, in the feature space without using any keypoint-level annotations.
Includes spatial partition to build relationships between hands and faces, and spatial attention to aggregate local and global features.
Part-level Temporal Modeling:

Implicitly mines the long-short term context to capture the relevant attributes over time.
Includes temporal partition to extract short-term representations from video clips, and temporal attention to complement the long-term representation.
The two modeling processes are complementary, with the spatial module focusing on appearance cues and the temporal module capturing motion changes. The final fused representation is used for sign language classification.
Extensive experiments demonstrate that StepNet achieves competitive performance on three commonly-used sign language recognition benchmarks: 56.89% Top-1 accuracy on WLASL, 77.2% on NMFs-CSL, and 77.1% on BOBSL. The proposed method is also compatible with optical flow input and can produce superior performance when fused.

Thống kê

Sign language recognition is challenging due to similar-appearance hand gestures and the need to capture facial expressions.
Skeleton-based methods discard appearance attributes and suffer from noisy keypoint annotations.
RGB-based methods often ignore the fine-grained hand structure and geometric characteristics of sign language videos.

Trích dẫn

"Skeleton-based methods do not consider facial expressions, while RGB-based approaches usually ignore the fine-grained hand structure."
"Most pixels in sign videos are static, while the discriminative parts only take a few spaces in the frame. Therefore, the non-trivial slight hand movement and facial expression need ad-hoc optimization."

Thông tin chi tiết chính được chắt lọc từ

StepNet

by Xiaolong She... lúc arxiv.org 04-09-2024

https://arxiv.org/pdf/2212.12857.pdf

Yêu cầu sâu hơn

How can the proposed part-aware modeling approach be extended to other fine-grained action recognition tasks beyond sign language

The proposed part-aware modeling approach in StepNet can be extended to other fine-grained action recognition tasks beyond sign language by adapting the concept of capturing fine-grained details and relationships between different parts of the action. For instance, in tasks like gesture recognition, sports analysis, or medical gesture recognition, the model can be trained to focus on specific parts of the body or objects involved in the action. By incorporating spatial-temporal part-aware modules similar to those in StepNet, the model can learn to extract relevant features from different parts of the action sequence, enhancing the overall recognition accuracy.

What are the potential limitations of the current part-aware modeling and how can they be addressed in future work

One potential limitation of the current part-aware modeling approach in StepNet could be the complexity of capturing all fine-grained details and relationships between parts effectively. To address this limitation in future work, researchers could explore more advanced attention mechanisms to better focus on crucial parts of the action sequence. Additionally, incorporating multi-modal inputs, such as depth data or audio cues, could provide additional context for the model to improve its understanding of the action. Furthermore, conducting more extensive experiments on diverse datasets and evaluating the model's performance in real-world scenarios could help identify and address any limitations effectively.

How can the spatial-temporal cues captured by StepNet be leveraged for other sign language understanding tasks, such as sign language translation or generation

The spatial-temporal cues captured by StepNet can be leveraged for other sign language understanding tasks, such as sign language translation or generation, by utilizing the learned representations to enhance the performance of these tasks. For sign language translation, the spatial-temporal cues can provide valuable information about the nuances and context of the sign language gestures, improving the accuracy of translation models. In sign language generation, the spatial-temporal cues can be used to generate more realistic and contextually appropriate sign language sequences. By integrating the spatial-temporal cues from StepNet into existing sign language understanding models, researchers can potentially achieve better results in these tasks.