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Object detection is a critical technology for the intelligent analytical processing of images captured by drones. The objects usually come in various scales and can be extremely small. Existing detection methods are inherently based on pyramid hierarchy architectures to extract multi-scale features and provide better feature representation for small objects. Nevertheless, they inevitably dilute the representation of details in low-level features during top-down feature fusion and are totally unconcerned with whether the fused feature fits the objects of specific scales within a layer. Moreover, the pyramid can only implicitly fuse the spatial context, which makes the fused features cannot receive fine spatial location information for object localization. In this work, we propose an effective boundary-aware network with attention refinement and spatial interaction to tackle the above challenges. Specifically, we first present a highly effective yet simple boundary-aware detection head (BAH), which directly guides representation learning of object structure semantics in the prediction layer to preserve object-related boundary semantics. Additionally, the attentional feature parallel fusion (AFPF) module offers multi-scale feature encoding capability in a parallel triple fusion fashion and adaptively selects features appropriate for objects of certain scales. Furthermore, we design a spatial interactive module (SIM) to preserve fine spatial detail through cross-spatial feature association. Extensive experiments prove that the proposed network significantly outperforms the state-of-the-art methods, in which we achieve 33.1 mAP and 56.5 AP50 on the VisDrone benchmark, 63.4 mAP and 94 AP50 on the NWPU VHR-10 benchmark. The source code will be released. [ABSTRACT FROM AUTHOR]

Sketch-to-photo face generation has recently gained remarkable attention in computer vision and signal processing communities, because the sketches that employ concise lines are easily available and can describe significant facial attributes conveniently. Most existing sketch-to-photo works fail to maintain geometric structures and improve local details simultaneously, which limits their performance. In this work, we propose a two-stage sketch-to-photo generative adversarial network for face generation. In the first stage, we propose a semantic loss to maintain semantic consistency. In the second stage, we define the similar connected component and propose a color refinement loss to generate fine-grained details. Moreover, we introduce a multi-scale discriminator and design a patch-level local discriminator. We also propose a texture loss to enhance the local fidelity of synthesized images. Experiments show that our proposed method can significantly generate better results while preserving facial attributes than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Correlations in facial action units (AUs) convey significant information for AU detection yet have not been thoroughly exploited. Most existing methods learn the regional correlation distribution of each AU, or reason the dependencies among AUs. However, these methods typically either predefine the correlations based on prior knowledge, which often ignores useful information, or directly learn the correlations guided by AU detection, which often includes irrelevant information. To resolve these limitations, we propose a novel hybrid relational reasoning framework for AU detection. In particular, we propose to adaptively reason pixel-level correlations of each AU, under the constraint of predefined regional correlations by facial landmarks, as well as the supervision of AU detection. Moreover, we propose to adaptively reason AU-level correlations using a graph convolutional network, by considering both predefined AU relationships and learnable relationship weights. Our framework is beneficial for integrating the advantages of correlation predefinition and correlation learning. Extensive experiments demonstrate that our approach (i) soundly outperforms the state-of-the-art AU detection methods on the challenging BP4D, DISFA, and GFT benchmarks, and (ii) can precisely reason the regional correlation distribution of each AU. [ABSTRACT FROM AUTHOR]