Deep neural networks tend to reciprocate the bias of their training dataset. In object detection, the bias exists in the form of various imbalances such as class, background-foreground, and object size. In this paper, we denote size of an object as the number of pixels it covers in an image and size imbalance as the over-representation of certain sizes of objects in a dataset. We aim to address the problem of size imbalance in drone-based aerial image datasets. Existing methods for solving size imbalance are based on architectural changes that utilize multiple scales of images or feature maps for detecting objects of different sizes. We, on the other hand, propose a novel ARchitectUre-agnostic BAlanced Loss (ARUBA) that can be applied as a plugin on top of any object detection model. It follows a neighborhood-driven approach inspired by the ordinality of object size. We evaluate the effectiveness of our approach through comprehensive experiments on aerial datasets such as HRSC2016, DOTAv1.0, DOTAv1.5 and VisDrone and obtain consistent improvement in performance.

In this paper, we describe the design and evaluation of a scheduler (referred to as Everest) for allocating processors to services in high performance, multi-service routers. A scheduler for such routers is required to maximize the number of packets processed within a given delay tolerance, while isolating the performance of services from each other. The design of such a scheduler is novel and challenging because of three domain-specific characteristics: (1) difficult-to-predict and high packet arrival rates, (2) small delay tolerances of packets, and (3) significant overheads for switching allocation of processors from one service to another. These characteristics require that the scheduler be agile and wary simultaneously. Whereas agility enables the scheduler to react quickly to fluctuations in packet arrival rates, wariness prevents the scheduler from wasting computational resources in unnecessary context switches. We demonstrate that by balancing agility and wariness, Everest, as compared to conventional schedulers, reduces by more than an order of magnitude the average delay and the percentage of packets that experience delays greater than their tolerance. We describe a prototype implementation of Everest on Intel's IXP2400 network processor