Improving Scene Graph Generation with Superpixel-Based Interaction Learning

Recent advances in Scene Graph Generation (SGG) typically model the relationships among entities utilizing box-level features from pre-defined detectors. We argue that an overlooked problem in SGG is the coarse-grained interactions between boxes, which inadequately capture contextual semantics for relationship modeling, practically limiting the development of the field. In this paper, we take the initiative to explore and propose a generic paradigm termed Superpixel-based Interaction Learning (SIL) to remedy coarse-grained interactions at the box level. It allows us to model fine-grained interactions at the superpixel level in SGG. Specifically, (i) we treat a scene as a set of points and cluster them into superpixels representing sub-regions of the scene. (ii) We explore intra-entity and cross-entity interactions among the superpixels to enrich fine-grained interactions between entities at an earlier stage. Extensive experiments on two challenging benchmarks (Visual Genome and Open Image V6) prove that our SIL enables fine-grained interaction at the superpixel level above previous box-level methods, and significantly outperforms previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing box-level approaches in a plug-and-play fashion. In particular, SIL brings an average improvement of 2.0% mR (even up to 3.4%) of baselines for the PredCls task on Visual Genome, which facilitates its integration into any existing box-level method

Improving Detection in Aerial Images by Capturing Inter-Object Relationships

In many image domains, the spatial distribution of objects in a scene exhibits meaningful patterns governed by their semantic relationships. In most modern detection pipelines, however, the detection proposals are processed independently, overlooking the underlying relationships between objects. In this work, we introduce a transformer-based approach to capture these inter-object relationships to refine classification and regression outcomes for detected objects. Building on two-stage detectors, we tokenize the region of interest (RoI) proposals to be processed by a transformer encoder. Specific spatial and geometric relations are incorporated into the attention weights and adaptively modulated and regularized. Experimental results demonstrate that the proposed method achieves consistent performance improvement on three benchmarks including DOTA-v1.0, DOTA-v1.5, and HRSC 2016, especially ranking first on both DOTA-v1.5 and HRSC 2016. Specifically, our new method has an increase of 1.59 mAP on DOTA-v1.0, 4.88 mAP on DOTA-v1.5, and 2.1 mAP on HRSC 2016, respectively, compared to the baselines

Feedback RoI Features Improve Aerial Object Detection

Neuroscience studies have shown that the human visual system utilizes high-level feedback information to guide lower-level perception, enabling adaptation to signals of different characteristics. In light of this, we propose Feedback multi-Level feature Extractor (Flex) to incorporate a similar mechanism for object detection. Flex refines feature selection based on image-wise and instance-level feedback information in response to image quality variation and classification uncertainty. Experimental results show that Flex offers consistent improvement to a range of existing SOTA methods on the challenging aerial object detection datasets including DOTA-v1.0, DOTA-v1.5, and HRSC2016. Although the design originates in aerial image detection, further experiments on MS COCO also reveal our module's efficacy in general detection models. Quantitative and qualitative analyses indicate that the improvements are closely related to image qualities, which match our motivation

Detecting HI Galaxies with Deep Neural Networks in the Presence of Radio Frequency Interference

In neutral hydrogen (HI) galaxy survey, a significant challenge is to identify and extract the HI galaxy signal from observational data contaminated by radio frequency interference (RFI). For a drift-scan survey, or more generally a survey of a spatially continuous region, in the time-ordered spectral data, the HI galaxies and RFI all appear as regions which extend an area in the time-frequency waterfall plot, so the extraction of the HI galaxies and RFI from such data can be regarded as an image segmentation problem, and machine learning methods can be applied to solve such problems. In this study, we develop a method to effectively detect and extract signals of HI galaxies based on a Mask R-CNN network combined with the PointRend method. By simulating FAST-observed galaxy signals and potential RFI impacts, we created a realistic data set for the training and testing of our neural network. We compared five different architectures and selected the best-performing one. This architecture successfully performs instance segmentation of HI galaxy signals in the RFI-contaminated time-ordered data (TOD), achieving a precision of 98.64% and a recall of 93.59%.Comment: 17 pages, 9 figures, 1 tables. Accepted for publication in RA

Fretology - Gaining luthier perspectives toward informing project development

Fretology is an acoustic guitar research, preservation, and education project. To clarify the future direction of Fretology, the project investigated the attitudes and thoughts of luthiers towards Fretology through two main methods. Questionnaires were used to ascertain the basic profile of the luthiers and their interest in the project, while interviews were conducted to discuss in-depth attitudes towards Fretology and its future direction and possibilities. The team concluded that Fretology still has great potential and plenty of room for development

Urban Traffic Operation Pattern and Spatiotemporal Mode Based on Big Data (Taking Beijing Urban Area as an Example)

An analysis of urban traffic operation pattern and spatiotemporal mode is an important basis to solve the problems of traffic congestion, emergency and extreme weather. Traditional studies on the urban traffic operation pattern and spatiotemporal mode usually are restricted by issues as poor time effectiveness, large space scale and coarse time granularity of traffic flow data, thus this essay choose to use the urban traffic speed data based on floating vehicle trajectory to dissect the urban traffic operation pattern and spatiotemporal mode in Beijing in a multi-dimensional and fine granularity. Differences of features in weekdays and weekends are also compared. This paper reports that âtwo-peakâ mode is obvious in the urban traffic condition. Besides, the morning peak of weekends is postponed to 11-12 am, and the night peak appears shorter in 5 pm compared to weekdays. Finally, four modes of traffic and its driving mechanism are concluded

Stereotactic radiosurgery combined with immune checkpoint inhibitors for brain metastasis: A systematic review and meta-analysis

Many studies have reported the combination of radiosurgery and immune checkpoint inhibitors (ICI) in the treatment of brain metastasis, but these studies have not reached a consistent conclusion. Therefore, we conducted this systematic review and meta-analysis to evaluate the effect of combination therapy compared with radiosurgery alone on the prognosis of patients with brain metastasis. The Pubmed-MEDLINE and Ovid-EMBASE databases were comprehensively searched to identify relevant articles until May 5, 2022. The search results were filtered by the inclusion and exclusion criteria described in this paper. The pooled hazard ratios (HR) with 95% confidence intervals (CI) were presented as estimates effect to reflect the effect of combined therapy on each outcome. A total of 17 eligible studies covering 2079 patients were included in this meta-analysis. The pooled results showed that the use of targeted drugs could significantly improve the overall survival (HR = 0.62, 95%CI: 0.51–0.76; P<0.01), reduce the risk of local recurrence (HR = 0.48, 95%CI: 0.38–0.62; P<0.01) and distant brain recurrence (HR = 0.70, 95%CI: 0.50–0.97; P<0.05). Overall, SRS combined with ICIs could significantly improve overall survival, local control, and distant brain control of patients with brain metastasis compared to SRS alone, but the effect varies for different pathological types. Our results verified the rationality of the current treatment strategy for brain metastasis which emphasizes the combination of local and systematic therapy

Integrating Transcriptomic and ChIP-Seq Reveals Important Regulatory Regions Modulating Gene Expression in Myometrium during Implantation in Pigs

The myometrium is the outer layer of the uterus. Its contraction and steroidogenic activities are required for embryo implantation. However, the molecular mechanisms underlying its functions remain unknown in pigs. The myometrium includes the inner circular muscle (CM) and the outer longitudinal muscle (LM) layers. In this study, we collected the CM and LM samples from the mesometrial side (named M) of the uterus on days 12 (pre-implantation stage) and 15 (implantation stage) of pregnancy and day 15 of the estrous cycle. The transcriptomic results revealed distinct differences between the uterine CM and LM layers in early pregnancy: the genes expressed in the LM layer were mainly related to contraction pathways, whereas the transcriptional signatures in the CM layer on day 15 of pregnancy were primarily involved in the immune response processes. Subsequent comparisons in the CM layer between pregnant and cyclic gilts show that the transcriptional signatures of the CM layer are implantation-dependent. Next, we investigated the genome-wide profiling of histone H3 lysine 27 acetylation (H3K27ac) and histone H3 lysine 4 trimethylation (H3K4me3) in pig uterine CM and LM layers. The genomic regions that had transcriptional activity and were associated with the expression of genes in the two layers were characterized. Taken together, the regulatory regions identified in the study may contribute to modulating the gene expression in pig uterine CM and LM layers during implantation

An Intelligent Prediction Method of the Karst Curtain Grouting Volume Based on Support Vector Machine

The prediction of the grouting volume is a very important task in the grouting quality control. Because of the concealment and complexity of the karst curtain grouting engineering, there is little research on the prediction of the karst curtain grouting volume (KCGV), and the prediction is hindered by the practical problems of small samples, high dimensions, and nonlinearity. In the study, based on the basic idea of support vector machine (SVM), a multiparameter comprehensive intelligent prediction method of the KCGV is proposed, which overcomes the limitation of few sample data in practical engineering. This method takes the grouting construction conditions and the slurry conditions which control the slurry diffusion as the input parameters, which are the basic data which can be easily obtained in the field grouting process. This feature greatly improves the prediction accuracy and generalization performance of the method. The intelligent prediction method of the KCGV based on SVM is applied to a typical karst curtain grouting project. The mean absolute error of the prediction results is 3.47 L/m, and the mean absolute percentage error of the prediction results is 5.97%. The results show that the proposed prediction method has an excellent prediction effect on the KCGV and can provide practical and beneficial help for the field karst curtain grouting project