Few-shot object detection based on positive-sample improvement

Traditional object detectors based on deep learning rely on plenty of labeled samples,which are expensive to obtain.Few-shot object detection(FSOD)attempts to solve this problem,learning detection objects from a few labeled samples,but the performance is often unsatisfactory due to the scarcity of samples.We believe that the main reasons that restrict the performance of few-shot detectors are:(1)the positive samples is scarce,and(2)the quality of positive samples is low.Therefore,we put forward a novel few-shot object detector based on YOLOv4,starting from both improving the quantity and quality of positive samples.First,we design a hybrid multivariate positive sample augmentation(HMPSA)module to amplify the quantity of positive samples and increase positive sample diversity while suppressing negative samples.Then,we design a selective non-local fusion attention(SNFA)module to help the detector better learn the target features and improve the feature quality of positive samples.Finally,we optimize the loss function to make it more suitable for the task of FSOD.Experimental results on PASCAL VOC and MS COCO demonstrate that our designed few-shot object detector has competitive performance with other state-of-the-art detectors.

Object detection in crowded scenes via joint prediction

Detecting highly-overlapped objects in crowded scenes remains a challenging problem,especially for one-stage detector.In this paper,we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme,named YOLO-CS.Specifically,we give YOLOv4 the power to detect multiple objects in one cell.Center to our method is the carefully designed joint prediction scheme,which is executed through an assignment of bounding boxes and a joint loss.Equipped with the derived joint-object augmentation(DJA),refined regression loss(RL)and Score-NMS(SN),YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time.Furthermore,on the widely used general benchmark COCO,YOLOCS still has a good performance,indicating its robustness to various scenes.

Consensus by Chinese Expert Panel on Chlamydia trachomatis-Resistant and Chlamydia trachomatis-Persistent Infection

Chlamydia trachomatis （Ct） genital infection is the most common sexually transmitted disease （STD） in China and the US. The morbidity of Ct genital infection in China has increased from 32.48/100,000 in 2008 to 37.18/100,000 in 2015.[1] The major areas of Ct infections are concentrated in the Zhujiang Delta, Changjiang Delta, Minjiang Area, and West China. In these areas, the highest incidence of Ct infection reaches 615.99/100,000 citizens. In the US, there are 1,441,789 reported Ct, which include 627.2 females and 278.4 males per 100,000 population. It is now the most prevalent STD, with its rate increasing to 22% in males and 6% in females.[2] Ct genital infection can cause epididymitis, prostatitis, cervicitis, annexitis, infertility, and atopic pregnancy, which have been identified as the major public health problems.

Guidelines for the Diagnosis and Treatment of Rosacea in China(2021 Edition)

Rosacea is a chronic inflammatory skin disease that primarily affects the centrofacial areas and mainly manifests as recurrent flushing and erythema.In recent years,there has been progress in the understanding of the diagnosis and treatment of rosacea.Therefore,a group of dermatological experts updated the guidelines based on the 2016 expert consensus statement on rosacea diagnosis and treatment in China.These new guidelines propose diagnostic criteria for rosacea at different sites to further standardize the diagnosis and treatment of rosacea in China.