GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we highlight a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching achieves impressive performance on two public benchmarks, e.g., setting a new record on the ICDAR15-video dataset, and one novel test set with arbitrary-shaped text, while saving considerable training budgets. The code will be released at https://github.com/Hxyz-123/GoMatching

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

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

The Segment Anything Model (SAM), a profound vision foundation model pre-trained on a large-scale dataset, breaks the boundaries of general segmentation and sparks various downstream applications. This paper introduces Hi-SAM, a unified model leveraging SAM for hierarchical text segmentation. Hi-SAM excels in text segmentation across four hierarchies, including stroke, word, text-line, and paragraph, while realizing layout analysis as well. Specifically, we first turn SAM into a high-quality text stroke segmentation (TSS) model through a parameter-efficient fine-tuning approach. We use this TSS model to iteratively generate the text stroke labels in a semi-automatical manner, unifying labels across the four text hierarchies in the HierText dataset. Subsequently, with these complete labels, we launch the end-to-end trainable Hi-SAM based on the TSS architecture with a customized hierarchical mask decoder. During inference, Hi-SAM offers both automatic mask generation (AMG) mode and promptable segmentation mode. In terms of the AMG mode, Hi-SAM segments text stroke foreground masks initially, then samples foreground points for hierarchical text mask generation and achieves layout analysis in passing. As for the promptable mode, Hi-SAM provides word, text-line, and paragraph masks with a single point click. Experimental results show the state-of-the-art performance of our TSS model: 84.86% fgIOU on Total-Text and 88.96% fgIOU on TextSeg for text stroke segmentation. Moreover, compared to the previous specialist for joint hierarchical detection and layout analysis on HierText, Hi-SAM achieves significant improvements: 4.73% PQ and 5.39% F1 on the text-line level, 5.49% PQ and 7.39% F1 on the paragraph level layout analysis, requiring 20x fewer training epochs. The code is available at https://github.com/ymy-k/Hi-SAM.Comment: GitHub repository: https://github.com/ymy-k/Hi-SA

High-grade serous papillary ovarian carcinoma combined with nonkeratinizing squamous cell carcinoma of the cervix: a case report

Multiple primary malignant neoplasms are a rare gynecologic malignancy; particularly, cases originating from the heterologous organs, such as the ovary and cervix. Here, we report a case of two primary malignant neoplasms in a patient who had undergone laparoscopic radical hysterectomy + bilateral salpingo-oophorectomy + pelvic lymph node dissection + para-aortic lymphadenectomy + appendectomy + omentectomy + metastasectomy under general anesthesia. The patient experienced complete remission after six courses of postoperative chemotherapy with a standard Taxol and Carboplatin regimen. Genetic testing was performed to detect BRCA2 mutations, and poly (ADP-ribose) polymerase (PARP) inhibitors were used for maintenance therapy

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

Effects of Different Herbicides on the Control of Malachium aquaticum L. and Poa annua L.

In this experiment, the effects of 13 kinds of herbicides (or mixtures) on the control of two kinds of weeds were studied by pot cultivation with the broadleaf weed Malachium aquaticum L. and Gramineae weed Poa annua L. as the materials. The results showed that the herbicide MCPA-Na had the best and fastest control effect on Malachium aquaticum L., and all the Malachium aquaticum L. died 7 d after treatment; it was followed by the other four herbicides including MCPA-Na+clethodim, MCPA-Na+ quizalofop-p-ethyl, bentazon and nicosulfuron·atrazine, and all the Malachium aquaticum L. died 14 d after treatment. Atrazine was the best herbicide to control Gramineae weeds, followed by nicosulfuron·atrazine, and mesotrione·nicosulfuron·atrazine. The study on the application of field herbicide found that four herbicides including atrazine, mesotrione·nicosulfuron·atrazine, nicosulfuron·atrazine and bentazon had better control effect on weeds. The best herbicide for flax field was MCPA-Na + clethodim, followed by MCPA-Na and MCPA-Na + quizalofop-p-ethyl. The optimized herbicides and combinations had no harmful effects on the growth of corn and flax

Safety and efficacy of self-expandable metallic stent combined with 125I brachytherapy for the treatment of malignant obstructive jaundice

Abstract Background Several previous studies demonstrated that the combination of self-expandable metallic stents (SEMS) and 125I seed implantation might prolong stent patency and obtain survival benefits for malignant obstructive jaundice (MOJ) patients. However, these studies rarely mentioned a comparison between CT-guided intratumoral 125I seed implantation and intraluminal 125I seed strand insertion combined with stenting for the management of MOJ. This study aimed to further evaluate the safety and efficacy of SEMS combined with 125I brachytherapy in the management of unresectable MOJ. Methods Fifty-nine patients with unresectable MOJ were retrospectively included from March 2018 to June 2021. The main therapeutic outcomes were evaluated in terms of stent patency, and overall survival. Cumulative stent patency and overall survival rates were calculated by Kaplan–Meier survival analysis. Both clinical and treatment factors associated with survival were analyzed. Results Technical success was achieved in all patients. The clinical success rate was 94% (32/34) in the seeds group and 92% (23/25) in the control group, no significant difference was found (p =1.000). The median duration of stent patency was significantly longer in the 125I brachytherapy group compared with the control group (289 days vs. 88 days, respectively, p =0.001). The 125I brachytherapy group demonstrated a significantly better median overall survival rate than the control group (221 days vs. 78 days, respectively, p =0.001). In multivariate analysis, stents with 125I brachytherapy (p =0.004) was a significant favorable prognostic factor that affected patient survival. No significant difference was observed between CT-guided 125I seed implantation and 125I seed strand insertion in stent patency (p =0.268), and overall survival (p =0.483). Conclusion SEMS combined with 125I brachytherapy is safe and effective for treating MOJ. 125I brachytherapy may help to maintain stent patency and prolong overall survival. There was no significant difference between CT-guided 125I seed implantation with SEMS and 125I seed strand insertion with SEMS in stent patency and overall survival

The Accuracy and Precision of the Continuously Stored Data from Flash Glucose Monitoring System in Type 2 Diabetes Patients during Standard Meal Tolerance Test

Background. The purpose of this study was to investigate the accuracy of the continuously stored data from the Abbott FreeStyle Libre flash glucose monitoring (FGM) system in Chinese diabetes patients during standard meal tests when glucose concentrations were rapidly changing. Subjects and Methods. Interstitial glucose levels were monitored for 14 days in 26 insulin-treated patients with type 2 diabetes using the FGM system. Standard meal tests were conducted to induce large glucose swings. Venous blood glucose (VBG) was tested at 0, 30, 60, and 120 min after standard meal tests in one middle day of the first and second weeks, respectively. The corresponding sensor glucose values were obtained from interpolating continuously stored data points. Assessment of accuracy was according to recent consensus recommendations with median absolute relative difference (MARD) and Clarke and Parkes error grid analysis (CEG and PEG). Results. Among 208 paired sensor-reference values, 100% were falling within zones A and B of the Clarke and Parkes error grid analysis. The overall MARD was 10.7% (SD, 7.8%). Weighted least squares regression analysis resulted in high agreement between the FGM sensor glucose and VBG readings. The overall MTT results showed that FGM was lower than actual VBG, with MAD of 22.1 mg/dL (1.2 mmol/L). At VBG rates of change of -1 to 0, 0 to 1, 1 to 2, and 2 to 3 mg/dl/min, MARD results were 11.4% (SD, 8.7%), 9.4% (SD, 6.5%), 9.9% (SD, 7.5%), and 9.5% (SD, 7.7%). At rapidly changing VBG concentrations (>3 mg/dl/min), MARD increased to 19.0%, which was significantly higher than slow changing BG groups. Conclusions. Continuously stored interstitial glucose measurements with the FGM system were found to be acceptable to evaluate VBG in terms of clinical decision during standard meal tests. The continuously stored data from the FGM system appeared to underestimate venous glucose and performed less well during rapid glucose changes

A Randomized Study to Compare the Effects of Once-Weekly Dulaglutide Injection and Once-Daily Glimepiride on Glucose Fluctuation of Type 2 Diabetes Mellitus Patients: A 26-Week Follow-Up

Objective. To evaluate the effects of once-weekly dulaglutide injection and once-daily glimepiride on glucose fluctuation in patients with type 2 diabetes mellitus (T2DM) using the Continuous Glucose Monitoring System (CGMS). Methods. A total of 23 patients with T2DM were randomly assigned into two groups for 26 weeks: the dulaglutide group (n=13) and the glimepiride group (n=10). 72-hour CGMS was applied to all patients: before and after the treatment. General clinical data were collected and measured, such as fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c), tumor necrosis factor-α (TNF-α), 8-iso-prostaglandin F2α (8-iso-PGF2α), and interleukin-6 (IL-6). Results. HbA1c of the dulaglutide group was reduced from 8.38±0.93% to 6.68±0.73% after the treatment (P0.05). The Mean Blood Glucose (MBG) of the two groups declined significantly after therapy (P0.05). The percentage time (PT) (>10 mmol/L and 3.9-10 mmol/L) of the two groups was significantly changed after the treatment (P0.05). Conclusion. Once-weekly dulaglutide injection has the same effectiveness as daily glimepiride on lowering blood glucose and decreasing oxidation stress and inflammation and is more effective in controlling glucose fluctuation as compared with glimepiride. This trial is registered with ClinicalTrials.gov NCT01644500