DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. In this paper, we present DeepSolo, a simple detection transformer baseline that lets a single Decoder with Explicit Points Solo for text detection and recognition simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations and thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel, solving the sub-tasks in text spotting in a unified framework. Besides, we also introduce a text-matching criterion to deliver more accurate supervisory signals, thus enabling more efficient training. Quantitative experiments on public benchmarks demonstrate that DeepSolo outperforms previous state-of-the-art methods and achieves better training efficiency. In addition, DeepSolo is also compatible with line annotations, which require much less annotation cost than polygons. The code will be released.Comment: The code will be available at https://github.com/ViTAE-Transformer/DeepSol

Male Occult Breast Cancer First Manifesting as a Supraclavicular Lymph Node Metastasis: A Case Report

Male occult breast cancer has a lower incidence, later clinical stage at detection, poorer differentiation, worse prognosis, and metastasizes earlier than breast cancer in females due to its rarity and a lack of public awareness. We report a 64 year old male patient presenting with palpable masses in his left cervical region, in whom imaging was unremarkable, except for slightly enlarged axillary lymph nodes without increased metabolic activity and an area of high metabolic activity in the left supraclavicular fossa. The purpose of this case report is to present the imaging, pathological features, and our treatment for male OBC

DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer

Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling. In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation. To address these challenges, this paper proposes a concise Dynamic Point Text DEtection TRansformer network, termed DPText-DETR. In detail, DPText-DETR directly leverages explicit point coordinates to generate position queries and dynamically updates them in a progressive way. Moreover, to improve the spatial inductive bias of non-local self-attention in Transformer, we present an Enhanced Factorized Self-Attention module which provides point queries within each instance with circular shape guidance. Furthermore, we design a simple yet effective positional label form to tackle the side effect of the previous form. To further evaluate the impact of different label forms on the detection robustness in real-world scenario, we establish an Inverse-Text test set containing 500 manually labeled images. Extensive experiments prove the high training efficiency, robustness, and state-of-the-art performance of our method on popular benchmarks. The code and the Inverse-Text test set are available at https://github.com/ymy-k/DPText-DETR

The Simulation of the Recharging Method Based on Solar Radiation for an Implantable Biosensor

A method of recharging implantable biosensors based on solar radiation is proposed. Firstly, the models of the proposed method are developed. Secondly, the recharging processes based on solar radiation are simulated using Monte Carlo (MC) method and the energy distributions of sunlight within the different layers of human skin have been achieved and discussed. Finally, the simulation results are verified experimentally, which indicates that the proposed method will contribute to achieve a low-cost, convenient and safe method for recharging implantable biosensors

A Systematic Government-Driven Green Development Transformation Strategy with Chinese Characteristics: The Case Study of the Xining Metropolitan Area

In the 21st century, the tension between economic growth, resources and the environment in countries around the world is increasing, and the sustainable development of the economy and society is under great pressure. Green development has become the only way for countries to promote sustainable development. Generally, capitalist countries achieve their green development goals through increasingly strict environmental protection regulations, technological upgrading, industrial upgrading and global transfer based on market mechanisms and legal environments. Evidently, this green development strategy relies on the core position of Western countries in the global technological leadership and the global division of labor. However, limited in terms of their economic strength and by technical barriers, how can developing countries, led by China, in the marginal position in the global market competition, carry out green development transformation? In line with the “high-quality development” strategy, governments at all levels in China are actively exploring green development strategies with their own characteristics. Based on the Second Tibetan Plateau Scientific Expedition and Research and the face-to-face interview method, this paper summarizes a new strategy of systematic government-driven green development combining internal and external factors in the underdeveloped areas of inland China, which has gradually formed in the Xining metropolitan area (XMA) in the past 20 years. This strategy has the following characteristics: Firstly, during the period of rapid growth, the XMA areas have promoted each other through new urbanization and new industrialization and jointly promoted the formation of a green development turn in the new era. Secondly, the government is the core actor and driving force of China’s regional green development and has gradually formulated and implemented a series of policy systems during this development. Restricted by local economic backwardness and low industrial profits, the implementation of green government policies tends to be mandatory. The majority of urban residents and rural people support this transformation because they have benefited from the transformation process. Thirdly, this green development strategy is reflected in many aspects, such as industry, ecology, the environment, space and transportation, and is part of a systematic, green-oriented transformation. Fourthly, the advantages of the socialist system with Chinese characteristics are the guarantee of the green development strategy. It is noteworthy that this kind of green development transformation requires a large amount of “additional” investment and the “rapid” upgrade of the industry. Therefore, it requires more time and the understanding and assistance of all sectors of society

Learning Adaptive Spatial Regularization and Temporal-Aware Correlation Filters for Visual Object Tracking

Recently, discriminative correlation filters (DCF) based trackers have gained much attention and obtained remarkable achievements for their high efficiency and outstanding performance. However, undesirable boundary effects occur when the DCF-based trackers suffer from challenging situations, such as occlusion, background clutters, fast motion, and so on. To address these problems, this work proposes a novel adaptive spatial regularization and temporal-aware correlation filters (ASTCF) model to deal with the boundary effects which occur in the correlation filters tracking. Firstly, our ASTCF model learns a more robust correlation filter template by introducing spatial regularization and temporal-aware components into the objective function. The adaptive spatial regularization provides a more robust appearance model to handle the large appearance changes at different times; meanwhile, the temporal-aware constraint can enhance the time continuity and consistency of this model. They make correlation filters model more discriminating, and also reduce the influence of the boundary effects during the tracking process. Secondly, the objective function can be transformed into three sub-problems with closed-form solutions and effectively solved via the alternating direction method of multipliers (ADMM). Finally, we compare our tracker with some representative methods and evaluate using three different benchmarks, including OTB2015, VOT2018 and LaSOT datasets, where the experimental results demonstrate the superiority of our tracker on most of the performance criteria compared with the existing trackers

Learning Adaptive Spatial Regularization and Temporal-Aware Correlation Filters for Visual Object Tracking

Recently, discriminative correlation filters (DCF) based trackers have gained much attention and obtained remarkable achievements for their high efficiency and outstanding performance. However, undesirable boundary effects occur when the DCF-based trackers suffer from challenging situations, such as occlusion, background clutters, fast motion, and so on. To address these problems, this work proposes a novel adaptive spatial regularization and temporal-aware correlation filters (ASTCF) model to deal with the boundary effects which occur in the correlation filters tracking. Firstly, our ASTCF model learns a more robust correlation filter template by introducing spatial regularization and temporal-aware components into the objective function. The adaptive spatial regularization provides a more robust appearance model to handle the large appearance changes at different times; meanwhile, the temporal-aware constraint can enhance the time continuity and consistency of this model. They make correlation filters model more discriminating, and also reduce the influence of the boundary effects during the tracking process. Secondly, the objective function can be transformed into three sub-problems with closed-form solutions and effectively solved via the alternating direction method of multipliers (ADMM). Finally, we compare our tracker with some representative methods and evaluate using three different benchmarks, including OTB2015, VOT2018 and LaSOT datasets, where the experimental results demonstrate the superiority of our tracker on most of the performance criteria compared with the existing trackers