With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face recognition networks are subject to attacks. Generating adversarial examples is an effective attack method, which misleads the face recognition system through obfuscation attack (rejecting a genuine subject) or impersonation attack (matching to an impostor). In this paper, we introduce a novel GAN, Attentional Adversarial Attack Generative Network (A³GN), to generate adversarial examples that mislead the network to identify someone as the target person not misclassify inconspicuously. For capturing the geometric and context information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces a face recognition network as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person.

In the rapidly advancing realm of visual generation, diffusion models have revolutionized the landscape, marking a significant shift in capabilities with their impressive text-guided generative functions. However, relying solely on text for conditioning these models does not fully cater to the varied and complex requirements of different applications and scenarios. Acknowledging this shortfall, a variety of studies aim to control pre-trained text-to-image (T2I) models to support novel conditions. In this survey, we undertake a thorough review of the literature on controllable generation with T2I diffusion models, covering both the theoretical foundations and practical advancements in this domain. Our review begins with a brief introduction to the basics of denoising diffusion probabilistic models (DDPMs) and widely used T2I diffusion models. Additionally, we provide a detailed overview of research in this area, categorizing it from the condition perspective into three directions: generation with specific conditions, generation with multiple conditions, and universal controllable generation. For each category, we analyze the underlying control mechanisms and review representative methods based on their core techniques.

Mainstream multi-object tracking methods exploit appearance information and/or motion information to achieve interframe association. However, dealing with similar appearance and occlusion is a challenge for appearance information, while motion information is limited by linear assumptions and is prone to failure in nonlinear motion patterns. In this work, we disregard appearance clues and propose a pure motion tracker to address the above issues. It dexterously utilizes Transformer to estimate complex motion and achieves high-performance tracking with low computing resources. Furthermore, contrastive learning is introduced to optimize feature representation for robust association. Specifically, we first exploit the long-range modeling capability of Transformer to mine intention information in temporal motion and decision information in spatial interaction and introduce prior detection to constrain the range of motion estimation. Then, we introduce contrastive learning as an auxiliary task to extract reliable motion features to compute affinity and introduce bidirectional matching to improve the affinity computation distribution. In addition, given that both tasks are dedicated to narrowing the embedding distance between the motion features of the tracked object and the detection features, we design a joint-motion-and-association framework to unify the above two tasks in one framework for optimization. The experimental results achieved with three benchmark datasets, MOT17, MOT20 and DanceTrack, verify the effectiveness of our proposed method. Compared with state-of-the-art methods, the proposed STDFormer sets a new state-of-the-art on DanceTrack and achieves competitive performance on MOT17 and MOT20. This demonstrates the advantage of our method in handling associations under similar appearance, occlusion or nonlinear motion. At the same time, the significant advantages of the proposed method over Transformer-based and contrastive learning-based methods suggest a new direction for the application of Transformer and contrastive learning in MOT. In addition, to verify the generalization of STDFormer in unmanned aerial vehicle (UAV) videos, we also evaluate STDFormer on VisDrone2019. The results show that STDFormer achieves state-of-the-art performance on VisDrone2019, which proves that it can handle small-scale object associations in UAV videos well. The code is available at https://github.com/Xiaotong-Zhu/STDFormer.

Object detection in remote sensing images is challenging due to the dense distribution and arbitrary angle of the objects. It is a consensus that the oriented bounding box (OBB) is more suitable to represent the aerial objects. However, there are some extreme cases in regression-based OBB detection that make the regression target discontinuous, resulting in poor performance. In this paper, an analysis of the formats of OBB and the problems in its regression is presented, following with an exploration of transform localization from regression to keypoint estimation, which could be applied to avoid the problem of discontinuous regression target. Our novel method is called Object-wise Point-guided Localization Detector (OPLD). Continuously, a new prediction of center-point is introduced to refine the results, as the truncation problem caused by the cut graph. Lastly, in order to figure the problem of inconsistency between the localization quality and the classification score, both the endpoint scores and the classification score are adopted weighting as a result score. Experimental results are based on two widely used datasets, i.e., DOTA and HRSC2016. OPLD achieve 76.43% mAP and 78.35% mAP in oriented bounding boxes (OBB) and horizontal bounding boxes (HBB) tasks of DOTA-v1.0, which achieves state-of-the-art performance, respectively. Project page at https://github.com/yf19970118/OPLD-Pytorch.