Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model

The Segment Anything Model (SAM) stands as a foundational framework for image segmentation. While it exhibits remarkable zero-shot generalization in typical scenarios, its advantage diminishes when applied to specialized domains like medical imagery and remote sensing. To address this limitation, this paper introduces Conv-LoRA, a simple yet effective parameter-efficient fine-tuning approach. By integrating ultra-lightweight convolutional parameters into Low-Rank Adaptation (LoRA), Conv-LoRA can inject image-related inductive biases into the plain ViT encoder, further reinforcing SAM's local prior assumption. Notably, Conv-LoRA not only preserves SAM's extensive segmentation knowledge but also revives its capacity of learning high-level image semantics, which is constrained by SAM's foreground-background segmentation pretraining. Comprehensive experimentation across diverse benchmarks spanning multiple domains underscores Conv-LoRA's superiority in adapting SAM to real-world semantic segmentation tasks.Comment: Accepted at ICLR 2024 Conferenc

A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data

The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing research typically uses the customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, we find that such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendation into language matching problem by taking product title description as model input to consider product functionality. We design a new transformation method to de-noise the signals derived from production data. In addition, we consider multilingual support from the engineering point of view. Our proposed end-to-end transformer-based model achieves both successes from offline and online experiments. The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.Comment: 6 pages, 3 figures, 5 tables, accepted in 21st IEEE International Conference on Machine Learning and Application

Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance

Deep learning has made great strides for object detection in images. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Compression is often achieved by reducing either spatial or amplitude resolution or, at times, both, both of which have well-known effects on performance. Detection accuracy also depends on the distance of the object of interest from the camera. Our work examines the impact of spatial and amplitude resolution, as well as object distance, on object detection accuracy and computational cost. We develop a resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of scales in the feature pyramid and detection head based on the spatial resolution of the input image. To train and evaluate this new method, we created a dataset of images with diverse spatial and amplitude resolutions by combining images from the TJU and Eurocity datasets and generating different resolutions by applying spatial resizing and compression. We first show that RA-YOLO achieves a good trade-off between detection accuracy and inference time over a large range of spatial resolutions. We then evaluate the impact of spatial and amplitude resolutions on object detection accuracy using the proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that leads to the highest detection accuracy depends on the 'tolerated' image size. We further assess the impact of the distance of an object to the camera on the detection accuracy and show that higher spatial resolution enables a greater detection range. These results provide important guidelines for choosing the image spatial resolution and compression settings predicated on available bandwidth, storage, desired inference time, and/or desired detection range, in practical applications

Research progress on the STAT signaling pathway in pregnancy and pregnancy-associated disorders

Signal transducer and activator of transcription (STAT) proteins, pivotal regulators of signaling cascades, undergo activation in response to the stimulation of cytokines and growth factors, and participate in biological processes, including inflammation, immune responses, cell proliferation, and differentiation. During the process of pregnancy, STAT signaling is involved in regulating embryonic implantation, endometrial decidualization, and establishing and maintaining maternal-fetal immune tolerance. Increasing evidence suggests that aberrant STAT signaling contributes to the occurrence and development of pregnancy disorders, including repeated implantation failure (RIF), preeclampsia (PE), recurrent spontaneous abortion (RSA), preterm birth (PTB) and gestational diabetes mellitus (GDM). Elucidating the molecular mechanisms of the STAT signaling pathway holds promise for further understanding the establishment and maintenance of normal pregnancy, and thereby providing potent targets and strategic avenues for the prevention and management of ailments associated with pregnancy. In this review, we summarized the roles of the STAT signaling pathway and its related regulatory function in embryonic implantation, endometrial decidualization, and maternal-fetal immune tolerance. In conclusion, in-depth research on the mechanism of the STAT signaling pathway not only enhances our understanding of normal pregnancy processes but also offers STAT-based therapeutic approaches to protect women from the burden of pregnancy-related disorders

Suppression of MUC1-Overexpressing Tumors by a Novel MUC1/CD3 Bispecific Antibody

Mucin1 (MUC1) is abnormally glycosylated and overexpressed in a variety of epithelial cancers and plays a critical role in tumor progression. MUC1 has received remark attention as an oncogenic molecule and is considered a valuable tumor target for immunotherapy, while many monoclonal antibodies (mAbs) targeting MUC1-positive cancers in clinical studies lack satisfactory results. It would be highly desirable to develop an effective therapy against MUC1-expressing cancers. In this study, we constructed a novel T cell-engaging bispecific antibody (BsAb) targeting MUC1 and CD3 with the Fab-ScFv-IgG format. A high quality of MUC1-CD3 BsAb can be acquired through a standard method. Our study suggested that this BsAb could specifically bind to MUC1- and CD3-positive cells and efficiently enhance T cell activation, cytokine release, and cytotoxicity. Furthermore, our study demonstrated that this BsAb could potently redirect T cells to eliminate MUC1-expressing tumor cells in vitro and significantly suppress MUC1-positive tumor growth in a xenograft mouse model. Thus, T cell-engaging MUC1/CD3 BsAb could be an effective therapeutic approach to combat MUC1-positive tumors and our MUC1/CD3 BsAb could be a promising candidate in clinical applications for the treatment of MUC1-positive cancer patients

Dietary Supplementation with Lysozyme–Cinnamaldehyde Conjugates Enhances Feed Conversion Efficiency by Improving Intestinal Health and Modulating the Gut Microbiota in Weaned Piglets Infected with Enterotoxigenic <i>Escherichia coli</i>

This study aims to evaluate the efficacy of lysozyme–cinnamaldehyde conjugates (LC) as a potential alternative to antibiotics in treating piglets infected with enterotoxigenic Escherichia coli (ETEC). The results demonstrated that piglets fed with the LC diet exhibited lower rectal temperature and fecal scores at 9 h, 24 h, and 48 h post-ETEC challenge. Furthermore, LC supplementation led to significant improvements in the mechanical and immune barriers of the jejunum and ileum, as indicated by an increased villi-height-to-crypt-depth ratio (VCR) and the expression of tight junction proteins, mucin, and β-defensins. Furthermore, the LC diet lowered the levels of pro-inflammatory cytokines TNF-α and IL-1β in the plasma. Further analyses showed that the LC diet downregulated genes (specifically TLR4 and MyD88) linked to the TLRs/MyD88/NF-κB signaling pathway in the small intestine. Additionally, 16SrDNA sequencing data revealed that LC supplementation increased the α diversity of intestinal microorganisms and the relative abundance of Lactobacillus. In summary, the LC-supplemented diet effectively mitigated the adverse effects of E. coli K88, including intestinal barrier damage and inflammation. Furthermore, it improved the structure of the intestinal flora, ultimately contributing to better growth performance in piglets