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Selecting representative samples plays an indispensable role in many machine learning and computer vision applications under limited resources (e.g., limited communication bandwidth and computational power). Determinantal Point Process (DPP) is a widely used method for selecting the most diverse representative samples that can summarize a dataset. However, its adaptability to different tasks remains an open challenge, as it is challenging for DPP to perform task-specific tuning. In contrast, Rate-Distortion (RD) theory provides a way to measure task-specific diversity. However, optimizing RD for a data selection problem remains challenging because the quantity that needs to be optimized is the index set of the selected samples. To tackle these challenges, we first draw an inherent relationship between DPP and RD theory. Our theoretical derivation paves the way for taking advantage of both RD and DPP for a task-specific data selection. To this end, we propose a novel method for task-specific data selection for multi-level classification tasks, named RD-DPP. Empirical studies on seven different datasets using five benchmark models demonstrate the effectiveness of the proposed RD-DPP method. Our method also outperforms recent strong competing methods, while exhibiting high generalizability to a variety of learning tasks. 

Determinantal Point Process (DPP) is a powerful technique to enhance data diversity by promoting the repulsion of similar elements in the selected samples. Particularly, DPP-based Maximum A Posteriori (MAP) inference is used to identify subsets with the highest diversity. However, a commonly adopted presumption of all data samples being available at one point hinders its applicability to real-world scenarios where data samples are distributed across distinct sources with intermittent and bandwidth-limited connections. This paper proposes a distributed version of DPP inference to enhance multi-source data diversification under limited communication budgets. First, we convert the lower bound of the diversity-maximized distributed sample selection from matrix determinant optimization to a simpler form of the sum of individual terms. Next, a determinant-preserved sparse representation of selected samples is formed by the sink as a surrogate for collected samples and sent back to sources as lightweight messages to eliminate the need for raw data exchange. Our approach is inspired by the channel orthogonalization process of Multiple-Input Multiple-Output (MIMO) systems based on the Channel State Information (CSI). Extensive experiments verify the superiority of our scalable method over the most commonly used data selection methods, including GreeDi, Greedymax, random selection, and stratified sampling by a substantial gain of at least 12% reduction in Relative Diversity Error (RDE). This enhanced diversity translates to a substantial improvement in the performance of various downstream learning tasks, including multi-level classification (2%-4% gain in accuracy), object detection (2% gain in mAP), and multiple-instance learning (1.3% gain in AUC). 

Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where their individual visual perception quality is compromised by adverse weather conditions (haze as foggy weather), low illumination, winding roads, and crowded traffic. While previous CP methods have shown success in elevating perception quality, they often assume perfect communication conditions and unlimited transmission resources to share camera feeds, which may not hold in real-world scenarios. Also, they make no effort to select better helpers when multiple options are available.To cover the limitations of former methods, in this paper, we propose a novel approach to realize an optimized CP under constrained communications. At the core of our approach is recruiting the best helper from the available list of front vehicles to augment the visual range and enhance the Object Detection (OD) accuracy of the ego vehicle. In this two-step process, we first select the helper vehicles that contribute the most to CP based on their visual range and lowest motion blur. Next, we implement a radio block optimization among the candidate vehicles to further improve communication efficiency. We specifically focus on pedestrian detection as an exemplary scenario. To validate our approach, we used the CARLA simulator to create a dataset of annotated videos for different driving scenarios where pedestrian detection is challenging for an AV with compromised vision. Our results demonstrate the efficacy of our two-step optimization process in improving the overall performance of cooperative perception in challenging scenarios, substantially improving driving safety under adverse conditions. Finally, we note that the networking assumptions are adopted from LTE Release 14 Mode 4 side-link communication, commonly used for Vehicle-to-Vehicle (V2V) commun