Efficient Adaptation of Large Vision-Language Models for Continuous Sign Language Recognition

Core Concepts

A novel strategy, AdaptSign, is proposed to efficiently adapt large vision-language models like CLIP for continuous sign language recognition, by introducing lightweight modules to inject domain-specific knowledge while preserving the generalizability of the pretrained model.

Abstract

The paper presents a novel strategy, AdaptSign, to efficiently adapt large vision-language models like CLIP for the task of continuous sign language recognition (CSLR). The key challenges addressed are the massive model size and scarcity of available data, which limit the direct application of these powerful models to downstream tasks.
The main highlights are:

AdaptSign adopts a frozen CLIP model as the visual backbone and introduces several lightweight learnable modules on top to adapt the generic features for CSLR:

Attention & FFN Adaption: Adds Adapters in parallel with the attention and feedforward layers to calibrate the intermediate features.
Prefix Embedding: Appends learnable prefix embeddings to inject domain-specific knowledge.
Multiscale Aggregation: Aggregates features from different CLIP blocks to leverage multiscale information.
Cross-Frame Attention: Captures the temporal trajectories of body parts like hands and face across frames.

The proposed modules are quite lightweight, only incurring 3.2% extra computations compared to the frozen CLIP backbone, enabling high efficiency.

Extensive experiments show that despite being efficient, AdaptSign outperforms existing CSLR methods by a large margin across multiple benchmarks, including PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL.

Visualizations demonstrate that AdaptSign can effectively focus on the informative spatial regions and cross-frame trajectories that are crucial for understanding sign language.

Overall, the paper presents an effective and efficient strategy to adapt large vision-language models for the specialized task of continuous sign language recognition, achieving state-of-the-art performance while preserving the generalizability of the pretrained model.

Stats

The PHOENIX14 dataset contains 6,841 sentences with a vocabulary of 1,295 signs, divided into 5,672 training, 519 development, and 629 testing samples.
The PHOENIX14-T dataset contains 8,247 sentences with a vocabulary of 1,085 signs, divided into 7,096 training, 519 development, and 642 testing samples.
The CSL-Daily dataset contains 20,654 sentences, divided into 18,401 training, 1,077 development, and 1,176 testing samples.
The CSL dataset contains 25,000 videos, divided into training and testing sets by a ratio of 8:2.

Quotes

"The increase of web-scale weakly labelled image-text pairs have greatly facilitated the development of large-scale vision-language models (e.g., CLIP), which have shown impressive generalization performance over a series of downstream tasks."
"To enable high efficiency when adapting these large vision-language models (e.g., CLIP) to performing continuous sign language recognition (CSLR) while preserving their generalizability, we propose a novel strategy (AdaptSign)."
"Extensive experiments show that despite being efficient, AdaptSign is able to demonstrate superior performance across a series of CSLR benchmarks including PHOENIX14, PHOENIX14-T, CSL-Daily and CSL compared to existing methods."

Key Insights Distilled From

Improving Continuous Sign Language Recognition with Adapted Image Models

by Lianyu Hu,To... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08226.pdf

Deeper Inquiries

How can the proposed AdaptSign strategy be extended to other video-based tasks beyond sign language recognition, such as action recognition or video understanding?

The AdaptSign strategy can be extended to other video-based tasks by leveraging its key components and principles. For action recognition, the attention & FFN adaption module can be utilized to adapt generic visual features to specific action-related information. By incorporating prompt embeddings tailored to action categories and employing multi-scale aggregation to capture different levels of action details, the model can effectively learn discriminative features for action recognition. Additionally, the cross-frame attention module can be adapted to capture temporal dependencies and motion trajectories crucial for understanding actions in videos. By customizing these modules to the requirements of action recognition tasks, AdaptSign can be applied to improve performance in this domain.

What are the potential limitations or drawbacks of the AdaptSign approach, and how could they be addressed in future work?

One potential limitation of the AdaptSign approach could be the reliance on pre-trained models like CLIP, which may not always capture domain-specific features adequately. To address this limitation, future work could focus on incorporating domain-specific pre-training or fine-tuning strategies to enhance the model's ability to learn task-specific features. Additionally, the lightweight nature of the added modules in AdaptSign may limit the model's capacity to capture complex spatial and temporal relationships in videos. Future research could explore more sophisticated module designs or architectures to improve the model's capability to extract intricate video features. Moreover, the generalizability of AdaptSign across different datasets and tasks could be a challenge, and future work could investigate methods to enhance the model's adaptability to diverse video datasets and tasks.

Given the importance of understanding sign language for accessibility and inclusion, how can the insights from this work be leveraged to develop more accessible and inclusive technologies for the deaf and hard-of-hearing community?

The insights from the AdaptSign work can be leveraged to develop more accessible and inclusive technologies for the deaf and hard-of-hearing community by enhancing sign language recognition systems. By improving the accuracy and efficiency of continuous sign language recognition, technologies can be developed to facilitate real-time translation of sign language into text or speech, enabling better communication between individuals who use sign language and those who do not. These technologies can be integrated into various platforms such as video conferencing tools, educational platforms, and communication devices to bridge the communication gap between the deaf and hearing communities. Additionally, the lightweight and efficient nature of the AdaptSign approach can enable the deployment of sign language recognition systems on resource-constrained devices, making them more accessible to a wider range of users in different settings.