Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms

Advanced imitation learning with structures like the transformer is increasingly demonstrating its advantages in robotics. However, deploying these large-scale models on embedded platforms remains a major challenge. In this paper, we propose a pipeline that facilitates the migration of advanced imitation learning algorithms to edge devices. The process is achieved via an efficient model compression method and a practical asynchronous parallel method Temporal Ensemble with Dropped Actions (TEDA) that enhances the smoothness of operations. To show the efficiency of the proposed pipeline, large-scale imitation learning models are trained on a server and deployed on an edge device to complete various manipulation tasks.

Dual-path Frequency Discriminators for Few-shot Anomaly Detection

Few-shot anomaly detection (FSAD) is essential in industrial manufacturing. However, existing FSAD methods struggle to effectively leverage a limited number of normal samples, and they may fail to detect and locate inconspicuous anomalies in the spatial domain. We further discover that these subtle anomalies would be more noticeable in the frequency domain. In this paper, we propose a Dual-Path Frequency Discriminators (DFD) network from a frequency perspective to tackle these issues. Specifically, we generate anomalies at both image-level and feature-level. Differential frequency components are extracted by the multi-frequency information construction module and supplied into the fine-grained feature construction module to provide adapted features. We consider anomaly detection as a discriminative classification problem, wherefore the dual-path feature discrimination module is employed to detect and locate the image-level and feature-level anomalies in the feature space. The discriminators aim to learn a joint representation of anomalous features and normal features in the latent space. Extensive experiments conducted on MVTec AD and VisA benchmarks demonstrate that our DFD surpasses current state-of-the-art methods. Source code will be available.

DRNet: Decomposition and Reconstruction Network for Remote Physiological Measurement

Remote photoplethysmography (rPPG) based physiological measurement has great application values in affective computing, non-contact health monitoring, telehealth monitoring, etc, which has become increasingly important especially during the COVID-19 pandemic. Existing methods are generally divided into two groups. The first focuses on mining the subtle blood volume pulse (BVP) signals from face videos, but seldom explicitly models the noises that dominate face video content. They are susceptible to the noises and may suffer from poor generalization ability in unseen scenarios. The second focuses on modeling noisy data directly, resulting in suboptimal performance due to the lack of regularity of these severe random noises. In this paper, we propose a Decomposition and Reconstruction Network (DRNet) focusing on the modeling of physiological features rather than noisy data. A novel cycle loss is proposed to constrain the periodicity of physiological information. Besides, a plug-and-play Spatial Attention Block (SAB) is proposed to enhance features along with the spatial location information. Furthermore, an efficient Patch Cropping (PC) augmentation strategy is proposed to synthesize augmented samples with different noise and features. Extensive experiments on different public datasets as well as the cross-database testing demonstrate the effectiveness of our approach.