Lifelong Person Re-Identification (LReID) seeks to continuously train models across multiple target domains, enabling effective generalization in both known and unseen domains. Achieving a balance between “plasticity” (the ability to adapt to new knowledge) and “stability” (the capacity to prevent forgetting) is crucial in lifelong learning. However, most existing LReID methods primarily focus on enhancing model stability or plasticity, often neglecting the critical balance between them. Moreover, current LReID approaches largely rely on supervised learning, which necessitates large-scale pre-labeled datasets—a process that is both time-consuming and labor-intensive in practical applications. To address these challenges, this article proposes an Unsupervised LReID approach called the Affinity Harmonization Network (AHN). AHN includes an Old Domain Affinity Constraint (ODAC) module, which builds an expert model for the old domain to provide affinity relationships as references. This helps limit changes among old representations, enabling the model to integrate new knowledge while preserving compatibility with previous representations. To harmonize stability and plasticity while guiding the model in acquiring new knowledge, AHN incorporates a Current Domain Affinity Guidance (CDAG) module. This module builds an expert model for the new domain and uses the generated affinity relationships to assist in training the model. Furthermore, this article proposes the Old Domain Intra-class Variance Constraint (OIVC) module, which mitigates potential deviations in the intra-class variance of legacy samples by limiting the distance between replay samples and old domain camera prototypes. Extensive experiments demonstrate that our method achieves significant performance improvements over existing unsupervised lifelong ReID methods, with an average gain of 5.3% in mAP and 5.2% in Rank-1 accuracy.

Unsupervised Person Re-identification (Re-ID) aims to identify the same person shot from non-overlapping cameras without any annotated data. In this task, attributes such as contrast, saturation, and resolution of the camera cause the deviation in target features. Since the camera label is readily available, they are employed to achieve the constraints across cameras and smooth the deviations during the model training phase. However, features from the same camera are prone to generating false positives due to the identical camera properties, which induce camera deviations on pseudo-label assignment. To address this problem, this paper proposes a novel camera-unbiased method named Camera Deviation Elimination Learning (CDE-Learning). In CDE-Learning, the Camera Deviation Compensation (CDC) module is designed to align data distributions from disparate cameras to decouple camera information from identity information during the pseudo-label allocation. Our Camera Deviation Balancing (CDB) module integrates different camera constraints in a united loss and adjusts camera constraints by constructing contrastive pairs between intra-camera and inter-camera. After explicit constraints, the Camera Attribution Auxiliary (CAA) task predicts whether a pair of images originates from the same camera to implicitly enhance the capacity to distinguish the camera deviation. We demonstrated the superior performance of the proposed CDE-Learning on benchmark datasets.

Serverless computing offers resource efficiency but suffers from a heavyweight data plane. We present Palladium, a DPU-offloaded serverless data plane enabling distributed zero-copy communication. Palladium uses two-sided RDMA and cross-processor shared memory to mitigate limitations of wimpy DPU cores. Its DPU-enabled network engine (DNE) isolates RDMA resources and manages flows across tenants. By converting HTTP/TCP to RDMA at ingress, Palladium reduces protocol overhead on the critical path.

The growth of video volumes and increased DNN capabilities have led to a growing desire for video analytics, which demands intensive computation resources. Traditional resource provisioning strategies, such as configuring a cluster per peak utilization, lead to low resource efficiency. Serverless computing is a promising way to avoid wasteful resource provisioning since video analytics regularly encounters bursty input workloads and fine-grained video content dynamics. For serverless-based video analytics, the application configuration (frame rate, detection model, and computation resources) will impact several metrics, such as computation cost and analytics accuracy. In this paper, we investigate the joint configuration adjustment problem for video knobs and computation resources provided by the serverless platform. We propose an algorithm that can efficiently adapt configurations for video streams to address two key challenges in serverless-based video analytics systems, including the complex relationships between the configurations and the key performance metrics, and the dynamically best configuration. Our adaptive configuration adjustment algorithm is developed based on Markov approximation to minimize the computation cost. To guarantee the accuracy, we then design the keyframe selection algorithm based on the secretary algorithm to identify significant changes in video content. We have developed a prototype over AWS Lambda and conducted extensive experiments with real-world video streams. The results show that our algorithm can greatly reduce the computation cost under the constraint of target accuracy.

The growth of video volumes and increased DNN capabilities have led to a growing desire for video analytics, which demands intensive computation resources. Traditional resource provisioning strategies, such as configuring a cluster per peak utilization, lead to low resource efficiency. Serverless computing is a promising way to avoid wasteful resource provisioning since video analytics regularly encounters bursty input workloads and finegrained video content dynamics. For serverless-based video analytics, the application configuration (frame rate, detection model, and computation resources) will impact several metrics, such as computation cost and analytics accuracy. In this paper, we investigate the joint configuration adjustment problem for video knobs and computation resources provided by the serverless platform. We propose an algorithm that can efficiently adapt configurations for video streams to address two key challenges in serverless-based video analytics systems, including the complex relationships between the configurations and the key performance metrics, and the dynamically best configuration. Our algorithm is developed based on Markov approximation to minimize the computation cost within an accuracy constraint. We have developed a prototype over AWS Lambda and conducted extensive experiments with real-world video streams. The results show that our algorithm can greatly reduce the computation cost under the constraint of target accuracy.