Towards Adaptive Subspace Detection in Heterogeneous Environment

In this paper, we aim to take one step forward to the scenario where an adaptive subspace detection framework is required to detect subspace signals in non-stationary environments. Despite the fact that this scenario is more realistic, the existing studies in detection theory mostly rely on homogeneous, or partially homogeneous assumptions in the environments for their design process meaning that the covariance matrices of primary and secondary datasets are exactly the same or different up to a scale factor. In this study, we allow some partial information of the train covariance matrix to be shared with the primary dataset, but the covariance matrix in the primary set can be entirely different in the structure. This is particularly true in radar systems where the secondary set is collected in distinct spatial and time zones. We design a Generalized Likelihood Ratio Test (GLRT) based detector where the noise is multivariate Gaussian and the subspace interference is assumed to be known. The simulation results reveal the superiority of the proposed approach in comparison with conventional detectors for such a realistic and general scenario

Evolution of the Sasanian defences of the Gorgan Plain

Aucune région du monde antique ne possède une concentration de fortifications militaires semblable à celle de la plaine de Gorgan. C’est aussi ici que nous trouvons la plus longue barrière linéaire renforcée de forts du monde de l’Antiquité tardive. Exception faite des forteresses urbaines, l’infrastructure militaire sassanide éclipse celle de l’État romain tardif. Cet article retrace l’évolution de la construction des infrastructures militaires depuis l’émergence soudaine des fortifications géométriques à la fin du ive ou au début du ve siècle jusqu’à leur abandon dans la première moitié du viie siècle. L’essor initial peut avoir été le résultat d’une pression hostile croissante, dans le nord et le nord-est de l’empire, à partir de la fin du ive siècle. La construction de fortifications a atteint son apogée au ve siècle, mais c’est au vie siècle que les forts du mur de Gorgan construits au ve siècle ont peut-être été occupés le plus densément. Le système a été maintenu jusqu’au viie siècle, bien qu’un certain nombre de fortifications dans l’arrière-pays ont vraisemblablement été abandonnées avant, et il n’y a pas encore de preuves de la construction de nouvelles installations dans les dernières décennies de la domination sassanide. Cet investissement massif a non seulement protégé la plaine de Gorgan mais aussi formé l’épine dorsale des défenses sassanides, vitales pour protéger le coeur de l’empire. Il a par ailleurs permis à l’empire de lancer des opérations militaires sur d’autres frontières

Box It to Bind It: Unified Layout Control and Attribute Binding in T2I Diffusion Models

While latent diffusion models (LDMs) excel at creating imaginative images, they often lack precision in semantic fidelity and spatial control over where objects are generated. To address these deficiencies, we introduce the Box-it-to-Bind-it (B2B) module - a novel, training-free approach for improving spatial control and semantic accuracy in text-to-image (T2I) diffusion models. B2B targets three key challenges in T2I: catastrophic neglect, attribute binding, and layout guidance. The process encompasses two main steps: i) Object generation, which adjusts the latent encoding to guarantee object generation and directs it within specified bounding boxes, and ii) attribute binding, guaranteeing that generated objects adhere to their specified attributes in the prompt. B2B is designed as a compatible plug-and-play module for existing T2I models, markedly enhancing model performance in addressing the key challenges. We evaluate our technique using the established CompBench and TIFA score benchmarks, demonstrating significant performance improvements compared to existing methods. The source code will be made publicly available at https://github.com/nextaistudio/BoxIt2BindIt

Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art

Transformers have rapidly gained popularity in computer vision, especially in the field of object recognition and detection. Upon examining the outcomes of state-of-the-art object detection methods, we noticed that transformers consistently outperformed well-established CNN-based detectors in almost every video or image dataset. While transformer-based approaches remain at the forefront of small object detection (SOD) techniques, this paper aims to explore the performance benefits offered by such extensive networks and identify potential reasons for their SOD superiority. Small objects have been identified as one of the most challenging object types in detection frameworks due to their low visibility. We aim to investigate potential strategies that could enhance transformers' performance in SOD. This survey presents a taxonomy of over 60 research studies on developed transformers for the task of SOD, spanning the years 2020 to 2023. These studies encompass a variety of detection applications, including small object detection in generic images, aerial images, medical images, active millimeter images, underwater images, and videos. We also compile and present a list of 12 large-scale datasets suitable for SOD that were overlooked in previous studies and compare the performance of the reviewed studies using popular metrics such as mean Average Precision (mAP), Frames Per Second (FPS), number of parameters, and more. Researchers can keep track of newer studies on our web page, which is available at \url{https://github.com/arekavandi/Transformer-SOD}