Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs

Label assignment plays a significant role in modern object detection models. Detection models may yield totally different performances with different label assignment strategies. For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold. Early object detectors simply utilize the fixed threshold for all training samples, while recent detection algorithms focus on adaptive thresholds based on the distribution of the IoUs to the ground truth boxes. In this paper, we introduce a simple while effective approach to perform label assignment dynamically based on the training status with predictions. By introducing the predictions in label assignment, more high-quality samples with higher IoUs to the ground truth objects are selected as the positive samples, which could reduce the discrepancy between the classification scores and the IoU scores, and generate more high-quality boundary boxes. Our approach shows improvements in the performance of the detection models with the adaptive label assignment algorithm and lower bounding box losses for those positive samples, indicating more samples with higher-quality predicted boxes are selected as positives

Aphid cluster recognition and detection in the wild using deep learning models

Abstract Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community

Regulation of a novel isoform of Receptor Expression Enhancing Protein REEP6 in rod photoreceptors by bZIP transcription factor NRL

The Maf-family leucine zipper transcription factor NRL is essential for rod photoreceptor development and functional maintenance in the mammalian retina. Mutations in NRL are associated with human retinopathies, and loss of Nrl in mice leads to a cone-only retina with the complete absence of rods. Among the highly down-regulated genes in the Nrl(−/−) retina, we identified receptor expression enhancing protein 6 (Reep6), which encodes a member of a family of proteins involved in shaping of membrane tubules and transport of G-protein coupled receptors. Here, we demonstrate the expression of a novel Reep6 isoform (termed Reep6.1) in the retina by exon-specific Taqman assay and rapid analysis of complementary deoxyribonucleic acid (cDNA) ends (5′-RACE). The REEP6.1 protein includes 27 additional amino acids encoded by exon 5 and is specifically expressed in rod photoreceptors of developing and mature retina. Chromatin immunoprecipitation assay identified NRL binding within the Reep6 intron 1. Reporter assays in cultured cells and transfections in retinal explants mapped an intronic enhancer sequence that mediated NRL-directed Reep6.1 expression. We also demonstrate that knockdown of Reep6 in mouse and zebrafish resulted in death of retinal cells. Our studies implicate REEP6.1 as a key functional target of NRL-centered transcriptional regulatory network in rod photoreceptors

Organizational attractiveness is in the eye of the beholder: the interaction of demographic characteristics with foreignness

Our analysis of 4605 individual evaluations of the 60 companies in the Reputation Quotient Annual 2000 study suggests that foreign-headquartered companies are less attractive employers, whereas more international companies are more attractive. Moreover, we find that gender, race, respondent age and educational level significantly interact with our foreignness variables in predicting company attractiveness. Journal of International Business Studies (2006) 37, 666–686. doi:10.1057/palgrave.jibs.8400218