ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition

Visual chart recognition systems are gaining increasing attention due to the growing demand for automatically identifying table headers and values from chart images. Current methods rely on keypoint detection to estimate data element shapes in charts but suffer from grouping errors in post-processing. To address this issue, we propose ChartDETR, a transformer-based multi-shape detector that localizes keypoints at the corners of regular shapes to reconstruct multiple data elements in a single chart image. Our method predicts all data element shapes at once by introducing query groups in set prediction, eliminating the need for further postprocessing. This property allows ChartDETR to serve as a unified framework capable of representing various chart types without altering the network architecture, effectively detecting data elements of diverse shapes. We evaluated ChartDETR on three datasets, achieving competitive results across all chart types without any additional enhancements. For example, ChartDETR achieved an F1 score of 0.98 on Adobe Synthetic, significantly outperforming the previous best model with a 0.71 F1 score. Additionally, we obtained a new state-of-the-art result of 0.97 on ExcelChart400k. The code will be made publicly available

A Study on High and Low Temperature Rheological Properties and Oil Corrosion Resistance of Epoxy Resin/SBS Composite Modified Bitumen

In order to investigate the high and low temperature rheological properties and fuel corrosion resistance of epoxy resin on SBS modified asphalt, epoxy resin/SBS composite modified asphalt (ER/SBS) was prepared by high-speed shear. Moreover, composite modified bitumen with different proportions were designed based on the uniform design method and the basic performance index test was performed. The optimal composite mixing ratio of the ER and SBS modifier in composite modified asphalt (2.3% and 3.8%, respectively) was determined. Temperature scanning and a multiple stress creep test (MSCR) on ER/SBS composite modified asphalt with different ER content before and after oil corrosion was carried out using a dynamic shear rheometer (DSR). In addition, the high temperature rheological properties of different ER contents and composite modified asphalt after oil corrosion were evaluated by combing DSR measurements with the test data. The creep stiffness (S) and creep rate (m) indexes were obtained by a bending rheometer (BBR), and the effect of ER on the low-temperature rheological properties of SBS modified bitumen was investigated. The influence of the modifier incorporation on the micromorphology of asphalt and the change of micromorphology of asphalt after oil corrosion were analyzed by fluorescence microscopy. The test results show that the incorporation of 2.3% ER and 3.8% SBS can effectively improve the high temperature performance of SBS modified asphalt under the premise of cost saving. Moreover, the composite modified asphalt doped with ER can effectively improve the resistance of SBS modified asphalt to fuel corrosion at high temperatures, and the greatest improvement in the oil corrosion resistance of composite modified asphalt is observed at the ER content of 2.3%

Boosting for transfer learning

Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identicaldistribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model