Progressive Transformation Learning for Leveraging Virtual Images in Training

To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.Comment: CVPR 2023 (Selected as Highlight

Abstract COMPILING DATAFLOW PROGRAMS FOR DIGITAL SIGNAL PROCESSING

The synchronous dataflow (SDF) model has proven efficient for represent-ing an important class of digital signal processing algorithms. The main property of this model is that the number of data values produced and consumed by each computation is fixed and known at compile-time. This thesis develops techniques to compile SDF-based graphical programs for embedded signal processing appli-cations into efficient uniprocessor implementations on microprocessors or pro-grammable digital signal processors. The main problems that we address are the minimization of code size and the minimization of the execution time and storage cost required to buffer intermediate results. The minimization of code size is an important problem since only limited amounts of memory are feasible under the speed and cost constraints of typical embedded system applications. We develop a class of scheduling algorithms that minimize code space requirements without sacrificing the efficiency of inline code. This is achieved through the careful organization of loops in the target pro-