RhythmMamba: Fast Remote Physiological Measurement with Arbitrary Length Videos

Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological signals from facial videos, holding great potential in various applications such as healthcare, affective computing, and anti-spoofing. Existing deep learning methods struggle to address two core issues of rPPG simultaneously: extracting weak rPPG signals from video segments with large spatiotemporal redundancy and understanding the periodic patterns of rPPG among long contexts. This represents a trade-off between computational complexity and the ability to capture long-range dependencies, posing a challenge for rPPG that is suitable for deployment on mobile devices. Based on the in-depth exploration of Mamba's comprehension of spatial and temporal information, this paper introduces RhythmMamba, an end-to-end Mamba-based method that employs multi-temporal Mamba to constrain both periodic patterns and short-term trends, coupled with frequency domain feed-forward to enable Mamba to robustly understand the quasi-periodic patterns of rPPG. Extensive experiments show that RhythmMamba achieves state-of-the-art performance with reduced parameters and lower computational complexity. The proposed RhythmMamba can be applied to video segments of any length without performance degradation. The codes are available at https://github.com/zizheng-guo/RhythmMamba.

RhythmFormer: Extracting rPPG Signals Based on Hierarchical Temporal Periodic Transformer

Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological signals based on facial videos, holding high potential in various applications such as healthcare, affective computing, anti-spoofing, etc. Due to the periodicity nature of rPPG, the long-range dependency capturing capacity of the Transformer was assumed to be advantageous for such signals. However, existing approaches have not conclusively demonstrated the superior performance of Transformer over traditional convolutional neural network methods, this gap may stem from a lack of thorough exploration of rPPG periodicity. In this paper, we propose RhythmFormer, a fully end-to-end transformer-based method for extracting rPPG signals by explicitly leveraging the quasi-periodic nature of rPPG. The core module, Hierarchical Temporal Periodic Transformer, hierarchically extracts periodic features from multiple temporal scales. It utilizes dynamic sparse attention based on periodicity in the temporal domain, allowing for fine-grained modeling of rPPG features. Furthermore, a fusion stem is proposed to guide self-attention to rPPG features effectively, and it can be easily transferred to existing methods to enhance their performance significantly. RhythmFormer achieves state-of-the-art performance with fewer parameters and reduced computational complexity in comprehensive experiments compared to previous approaches. The codes are available at https://github.com/zizheng-guo/RhythmFormer.

FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning

Due to the limited availability of data, existing few-shot learning methods trained from scratch fail to achieve satisfactory performance. In contrast, large-scale pre-trained models such as CLIP demonstrate remarkable few-shot and zero-shot capabilities. To enhance the performance of pre-trained models for downstream tasks, fine-tuning the model on downstream data is frequently necessary. However, fine-tuning the pre-trained model leads to a decrease in its generalizability in the presence of distribution shift, while the limited number of samples in few-shot learning makes the model highly susceptible to overfitting. Consequently, existing methods for fine-tuning few-shot learning primarily focus on fine-tuning the model's classification head or introducing additional structure. In this paper, we introduce a fine-tuning approach termed Feature Discrimination Alignment (FD-Align). Our method aims to bolster the model's generalizability by preserving the consistency of spurious features across the fine-tuning process. Extensive experimental results validate the efficacy of our approach for both ID and OOD tasks. Once fine-tuned, the model can seamlessly integrate with existing methods, leading to performance improvements. Our code can be found in https://github.com/skingorz/FD-Align.