Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, we propose a novel end-to-end Adaptively Aspect Ratio Multi-Scale Network (A ${}^2$ RMNet) to solve this problem. On the one hand, we design a multi-scale feature gate fusion network to adaptively integrate the multi-scale features of objects. This network is composed of gate fusion modules, refine blocks and region proposal networks. On the other hand, an aspect ratio attention network is leveraged to preserve the aspect ratios of objects, which alleviates the excessive shape distortions of objects caused by aspect ratio changes during training. Experiments show that the proposed A ${}^2$ RMNet significantly outperforms the previous state of the arts on the DOTA dataset, NWPU VHR-10 dataset, RSOD dataset and UCAS-AOD dataset by $5.73\%$ , $7.06\%$ , $3.27\%$ and $2.24\%$ , respectively.

Pixel-level segmentation has been widely used to improve object detection. Most of the existing methods refine detection features by adding the constraint of the segmentation branch or by simply embedding high-level segmentation features into detection features within the local receptive field. However, noisy segmentation features are unavoidable in real-word applications and can easily cause false positives. To address this problem, we propose a novel hierarchical context embedding module to effectively embed segmentation features into detection features. The idea of this module is to capture hierarchical context information that includes local objects or parts and nonlocal context features by learning multiple attention maps, and subsequently utilize interdependencies between features to recalibrate noisy segmentation features. Furthermore, we use this module in the proposed gated encoder-decoder network that adaptively aggregates feature maps of different resolutions based on the gate mechanism so that we can embed multiscale segmentation feature maps into detection features for more accurate detection of objects of all sizes. Experimental results demonstrate the effectiveness of the proposed method on the Pascal VOC 2012Seg dataset, the Pascal VOC dataset and the MS COCO dataset.

Object detection is a hot topic with various applications in computer vision, e.g., image understanding, autonomous driving, and video surveillance. Much of the progresses have been driven by the availability of object detection benchmark datasets, including PASCAL VOC, ImageNet, and MS COCO. However, object detection on the drone platform is still a challenging task, due to various factors such as view point change, occlusion, and scales. To narrow the gap between current object detection performance and the real-world requirements, we organized the Vision Meets Drone (VisDrone2018) Object Detection in Image challenge in conjunction with the 15th European Conference on Computer Vision (ECCV 2018). Specifically, we release a large-scale drone-based dataset, including 8, 599 images (6, 471 for training, 548 for validation, and 1, 580 for testing) with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc. Featuring a diverse real-world scenarios, the dataset was collected using various drone models, in different scenarios (across 14 different cities spanned over thousands of kilometres), and under various weather and lighting conditions. We mainly focus on ten object categories in object detection, i.e., pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle. Some rarely occurring special vehicles (e.g., machineshop truck, forklift truck, and tanker) are ignored in evaluation. The dataset is extremely challenging due to various factors, including large scale and pose variations, occlusion, and clutter background. We present the evaluation protocol of the VisDrone-DET2018 challenge and the comparison results of 38 detectors on the released dataset, which are publicly available on the challenge website: http://www.aiskyeye.com/ . We expect the challenge to largely boost the research and development in object detection in images on drone platforms.

Due to the flexible training requirement and the appealing generalization ability, unpaired image dehazing has received increasing attention in coping with real-world hazy images. However, most of the existing methods rely on the loose dehazing-hazing cycle constraint, which makes it hard to eliminate poor-quality dehazing results when using a powerful hazing network in the training process. To address this issue, this paper proposes a simple yet efficient Adversarial Deformation Constraint (ADC). More specifically, we sequentially perform two operations, i.e., dehazing and deformation, on a hazy image. In the training process, the dehazing branch is desired to be deformation-unaware, which requires that the output of these two operations remains constant regardless of their performing order. Adversarially, the deformation branch tends to maximize the difference in the outputs of these two operations when their performing orders are different. Through an additive image decomposition model, we verify that the ADC could regularize the solution space to push the dehazing error towards zero. Finally, by incorporating ADC into the common dehazing-hazing cycle constraint, we significantly improve the robustness of unpaired image dehazing. Experiments on multiple benchmark hazy image databases demonstrate the superiority of ADC over many state-of-the-art image dehazing methods. The source code of the proposed ADC-Net will be released on https://github.com/whrws/ADC-Net.

Few-shot segmentation (FSS) aims to segment the novel class with a few annotated images. Due to CLIP's advantages of aligning visual and textual information, the integration of CLIP can enhance the generalization ability of FSS model. However, even with the CLIP model, the existing CLIP-based FSS methods are still subject to the biased prediction towards base class, which is caused by the class-specific feature level interactions. To solve this issue, we propose a visual and textual Prior Guided Mask Assemble Network (PGMA-Net). It employs a class-agnostic mask assembly process to alleviate the bias, and formulates diverse tasks into a unified manner by assembling the prior through affinity. Specifically, the class-relevant textual and visual features are first transformed to class-agnostic prior in the form of probability map. Then, a Prior-Guided Mask Assemble Module (PGMAM) including multiple General Assemble Units (GAUs) is introduced. It considers diverse and plug-and-play interactions, such as visual-textual, inter- and intra-image, training-free, and high-order ones. Lastly, to ensure the class-agnostic ability, a Hierarchical Decoder with Channel-Drop Mechanism (HDCDM) is proposed to flexibly exploit the assembled masks and low-level features, without relying on any class-specific information. It achieves new state-of-the-art results in the FSS task, with mIoU of 77.6 on $\rm{PASCAL-}5^{i}$ and 59.4 on $\rm{COCO-}20^{i}$ in 1-shot scenario. Beyond this, we show that without extra re-training, the proposed PGMA-Net can solve bbox-level and cross-domain FSS, co-segmentation, zero-shot segmentation (ZSS) tasks, leading an any-shot segmentation framework capable of accommodating diverse weak or pixel annotations.

Referring image segmentation aims to segment the target object from the image according to the description of language expression. Due to the diversity of language expressions, word sequences in different orders often express different semantic information. The previous methods focus more on matching different words to different visual regions in the image separately, ignoring the global semantic understanding of language expression based on the sequence structure. To address this problem, we redesign a new recurrent network structure for referring image segmentation, called Cross-Modal Recurrent Semantic Comprehension Network (CRSCNet), to obtain a more comprehensive global semantic understanding through iterative cross-modal semantic reasoning. Specifically, in each iteration, we first propose a Dynamic SepConv to extract relevant visual features guided by language and further propose Language Attentional Feature Modulation to improve the feature discriminability, then propose a Cross-Modal Semantic Reasoning module to perform global semantic reasoning by capturing both linguistic and visual information, and finally updates and corrects the visual features of the predicted object based on semantic information. Moreover, we further propose a Cross-Modal ASPP to capture richer visual information referred to in the global semantics of the language expression from larger receptive fields. Extensive experiments demonstrate that our proposed network significantly outperforms previous state-of-the-art methods on multiple datasets.