Perceptual Generative Adversarial Networks for Small Object Detection

Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts

Integrated Face Analytics Networks through Cross-Dataset Hybrid Training

Face analytics benefits many multimedia applications. It consists of a number of tasks, such as facial emotion recognition and face parsing, and most existing approaches generally treat these tasks independently, which limits their deployment in real scenarios. In this paper we propose an integrated Face Analytics Network (iFAN), which is able to perform multiple tasks jointly for face analytics with a novel carefully designed network architecture to fully facilitate the informative interaction among different tasks. The proposed integrated network explicitly models the interactions between tasks so that the correlations between tasks can be fully exploited for performance boost. In addition, to solve the bottleneck of the absence of datasets with comprehensive training data for various tasks, we propose a novel cross-dataset hybrid training strategy. It allows "plug-in and play" of multiple datasets annotated for different tasks without the requirement of a fully labeled common dataset for all the tasks. We experimentally show that the proposed iFAN achieves state-of-the-art performance on multiple face analytics tasks using a single integrated model. Specifically, iFAN achieves an overall F-score of 91.15% on the Helen dataset for face parsing, a normalized mean error of 5.81% on the MTFL dataset for facial landmark localization and an accuracy of 45.73% on the BNU dataset for emotion recognition with a single model.Comment: 10 page

Parallel Space-Mapping Based Yield-Driven em Optimization Incorporating Trust Region Algorithm and Polynomial Chaos Expansion

Space mapping (SM) methodology has been recognized as a powerful tool for accelerating electromagnetic (EM)-based yield optimization. This paper proposes a novel parallel space-mapping based yield-driven EM optimization technique incorporating trust region algorithm and polynomial chaos expansion (PCE). In this technique, a novel trust region algorithm is proposed to increase the robustness of the SM surrogate in each iteration during yield optimization. The proposed algorithm updates the trust radius of each design parameter based on the effectiveness of minimizing the $l_{1}$ objective function using the surrogate, thereby increasing the robustness of the SM surrogate. Moreover, for the first time, parallel computation method is incorporated into SM-based yield-driven design to accelerate the overall yield optimization process of microwave structures. The use of parallel computation allows the surrogate developed in the proposed technique to be valid in a larger neighborhood than that in standard SM, consequently increasing the speed of finding the optimal yield solution in SM-based yield-driven design. Lastly, the PCE approach is incorporated into the proposed technique to further speed up yield verification on the fine model. Compared with the standard SM-based yield optimization technique with sequential computation, the propose

Fight Fire with Fire: Combating Adversarial Patch Attacks using Pattern-randomized Defensive Patches

Object detection has found extensive applications in various tasks, but it is also susceptible to adversarial patch attacks. Existing defense methods often necessitate modifications to the target model or result in unacceptable time overhead. In this paper, we adopt a counterattack approach, following the principle of "fight fire with fire," and propose a novel and general methodology for defending adversarial attacks. We utilize an active defense strategy by injecting two types of defensive patches, canary and woodpecker, into the input to proactively probe or weaken potential adversarial patches without altering the target model. Moreover, inspired by randomization techniques employed in software security, we employ randomized canary and woodpecker injection patterns to defend against defense-aware attacks. The effectiveness and practicality of the proposed method are demonstrated through comprehensive experiments. The results illustrate that canary and woodpecker achieve high performance, even when confronted with unknown attack methods, while incurring limited time overhead. Furthermore, our method also exhibits sufficient robustness against defense-aware attacks, as evidenced by adaptive attack experiments