Denoising-Diffusion Alignment for Enhancing Continuous Sign Language Recognition
Conceitos Básicos
The proposed Denoising-Diffusion Alignment (DDA) method leverages diffusion-based global alignment techniques to effectively align video and gloss sequence, facilitating global temporal context alignment and improving continuous sign language recognition performance.
Resumo
The content presents a novel Denoising-Diffusion Alignment (DDA) method for continuous sign language recognition (CSLR). The key challenges in CSLR are achieving cross-modality alignment between videos and gloss sequences, and capturing the global temporal context alignment among video clips.
The DDA consists of a denoising-diffusion autoencoder and a DDA loss function. The main steps are:
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Auxiliary condition diffusion: The video and gloss sequence representations are merged into a common low-dimensional latent space. The gloss sequence part is then gradually perturbed with Gaussian noise.
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Denoising-diffusion autoencoder: A Transformer-based diffusion encoder embeds the partially noised bimodal representations to learn both the video global context and the noised gloss sequence context. A diffusion decoder then decodes the latent representations to denoise the bimodal representations.
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DDA loss function: This loss not only performs denoising but also achieves alignment knowledge transfer, guiding the video representation to re-establish the global temporal context based on the global text context.
Experiments on three public benchmarks demonstrate that DDA achieves state-of-the-art performance and can generalize to other CSLR methods as a plug-and-play optimization.
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Denoising-Diffusion Alignment for Continuous Sign Language Recognition
Estatísticas
The proposed DDA method outperforms the keypoints-supervised TwoStream-SLR by 1.5% and 1.5% WERs on the dev and test set of PHOENIX-2014, and even surpasses it by 0.7% and 0.8% WERs on the dev and test set of PHOENIX-2014T.
DDA also exceeds the C2ST method by 0.6% and 0.5% WERs on the dev and test set of PHOENIX-2014, and by 0.3% and 0.4% WERs on the dev and test set of PHOENIX-2014T, as well as by 0.3% and 0.5% on both the dev and test sets on the CSL-Daily.
Citações
"The key challenge of CSLR is how to achieve the cross-modality alignment between videos and gloss sequences."
"The current cross-modality paradigms of CSLR overlook using the glosses context to guide the video clips for global temporal context alignment, which further affects the visual to gloss mapping and is detrimental to recognition performance."
"Our DDA loss not only performs the denoising but also achieves the alignment knowledge transfer."
Perguntas Mais Profundas
How can the proposed DDA method be extended to other cross-modal tasks beyond continuous sign language recognition
The proposed Denoising-Diffusion Alignment (DDA) method can be extended to other cross-modal tasks beyond continuous sign language recognition by adapting the framework to different modalities and contexts. One way to extend DDA is to apply it to tasks such as image captioning, where the goal is to generate textual descriptions of images. In this scenario, the visual modality (image) and textual modality (caption) can be aligned using DDA to improve the generation of accurate and contextually relevant captions for images. By incorporating the denoising-diffusion autoencoder approach and leveraging the global temporal context alignment, DDA can enhance the cross-modal alignment and improve the performance of image captioning systems.
What are the potential limitations of the denoising-diffusion autoencoder approach, and how could they be addressed in future work
The denoising-diffusion autoencoder approach may have some limitations that could be addressed in future work. One potential limitation is the computational complexity of training the autoencoder, especially when dealing with large-scale datasets or high-dimensional feature spaces. To address this, optimization techniques such as parallel processing, distributed computing, or model compression methods could be explored to improve the efficiency of training the denoising-diffusion autoencoder. Additionally, the denoising process in the autoencoder may introduce noise or distortions that could impact the quality of the aligned representations. Future research could focus on developing more robust denoising algorithms or incorporating additional regularization techniques to mitigate these effects and improve the overall performance of the autoencoder.
Given the importance of global temporal context alignment, how might the DDA framework be adapted to leverage additional modalities or contextual information to further improve recognition performance
To leverage additional modalities or contextual information and further improve recognition performance, the DDA framework can be adapted in several ways. One approach is to incorporate multimodal data sources, such as audio or text, in addition to video and gloss sequences, to capture a more comprehensive representation of the sign language communication. By integrating multiple modalities, DDA can learn richer and more informative representations that enhance the global temporal context alignment. Furthermore, contextual information, such as linguistic context or semantic relationships between signs, can be integrated into the alignment process to provide additional guidance for the model. By incorporating diverse modalities and contextual cues, the DDA framework can be adapted to achieve more robust and accurate recognition performance in various cross-modal tasks.
