Xuexue Li

Xuexue Li, Wenhui Diao, Yongqiang Mao et al.

Occlusion between objects is one of the overlooked challenges for object detection in UAV images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens more frequently than that in natural scenes. Compared to occlusion in natural scene images, occlusion in UAV images happens with feature confusion problem and local aggregation characteristic. And we found that extracting or localizing occlusion between objects is beneficial for the detector to address this challenge. According to this finding, the occlusion localization task is introduced, which together with the object detection task constitutes our occlusion-guided multi-task network (OGMN). The OGMN contains the localization of occlusion and two occlusion-guided multi-task interactions. In detail, an occlusion estimation module (OEM) is proposed to precisely localize occlusion. Then the OGMN utilizes the occlusion localization results to implement occlusion-guided detection with two multi-task interactions. One interaction for the guide is between two task decoders to address the feature confusion problem, and an occlusion decoupling head (ODH) is proposed to replace the general detection head. Another interaction for guide is designed in the detection process according to local aggregation characteristic, and a two-phase progressive refinement process (TPP) is proposed to optimize the detection process. Extensive experiments demonstrate the effectiveness of our OGMN on the Visdrone and UAVDT datasets. In particular, our OGMN achieves 35.0% mAP on the Visdrone dataset and outperforms the baseline by 5.3%. And our OGMN provides a new insight for accurate occlusion localization and achieves competitive detection performance.

Wenhui Diao, Haichen Yu, Kaiyue Kang et al.

Aerial Remote Sensing (ARS) vision tasks present significant challenges due to the unique viewing angle characteristics. Existing research has primarily focused on algorithms for specific tasks, which have limited applicability in a broad range of ARS vision applications. This paper proposes RingMo-Aerial, aiming to fill the gap in foundation model research in the field of ARS vision. A Frequency-Enhanced Multi-Head Self-Attention (FE-MSA) mechanism is introduced to strengthen the model's capacity for small-object representation. Complementarily, an affine transformation-based contrastive learning method improves its adaptability to the tilted viewing angles inherent in ARS tasks. Furthermore, the ARS-Adapter, an efficient parameter fine-tuning method, is proposed to improve the model's adaptability and performance in various ARS vision tasks. Experimental results demonstrate that RingMo-Aerial achieves SOTA performance on multiple downstream tasks. This indicates the practicality and efficacy of RingMo-Aerial in enhancing the performance of ARS vision tasks.

Xuexue Li

Most recent UAV (Unmanned Aerial Vehicle) detectors focus primarily on general challenge such as uneven distribution and occlusion. However, the neglect of scale challenges, which encompass scale variation and small objects, continues to hinder object detection in UAV images. Although existing works propose solutions, they are implicitly modeled and have redundant steps, so detection performance remains limited. And one specific work addressing the above scale challenges can help improve the performance of UAV image detectors. Compared to natural scenes, scale challenges in UAV images happen with problems of limited perception in comprehensive scales and poor robustness to small objects. We found that complementary learning is beneficial for the detection model to address the scale challenges. Therefore, the paper introduces it to form our scale-robust complementary learning network (SCLNet) in conjunction with the object detection model. The SCLNet consists of two implementations and a cooperation method. In detail, one implementation is based on our proposed scale-complementary decoder and scale-complementary loss function to explicitly extract complementary information as complement, named comprehensive-scale complementary learning (CSCL). Another implementation is based on our proposed contrastive complement network and contrastive complement loss function to explicitly guide the learning of small objects with the rich texture detail information of the large objects, named inter-scale contrastive complementary learning (ICCL). In addition, an end-to-end cooperation (ECoop) between two implementations and with the detection model is proposed to exploit each potential.

Xuexue Li

Instance segmentation of remote sensing images (RSIs) is an essential task for a wide range of applications such as land planning and intelligent transport. Instance segmentation of RSIs is constantly plagued by the unbalanced ratio of foreground and background and limited instance size. And most of the instance segmentation models are based on deep feature learning and contain operations such as multiple downsampling, which is harmful to instance segmentation of RSIs, and thus the performance is still limited. Inspired by the recent superior performance of prompt learning in visual tasks, we propose a new prompt paradigm to address the above issues. Based on the existing instance segmentation model, firstly, a local prompt module is designed to mine local prompt information from original local tokens for specific instances; secondly, a global-to-local prompt module is designed to model the contextual information from the global tokens to the local tokens where the instances are located for specific instances. Finally, a proposal's area loss function is designed to add a decoupling dimension for proposals on the scale to better exploit the potential of the above two prompt modules. It is worth mentioning that our proposed approach can extend the instance segmentation model to a promptable instance segmentation model, i.e., to segment the instances with the specific boxes prompt. The time consumption for each promptable instance segmentation process is only 40 ms. The paper evaluates the effectiveness of our proposed approach based on several existing models in four instance segmentation datasets of RSIs, and thorough experiments prove that our proposed approach is effective for addressing the above issues and is a competitive model for instance segmentation of RSIs.