Representation Learning with Fine-grained Patterns

With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most of existing algorithms on benchmark data sets. Many efforts have been devoted to studying the mechanism of deep learning. One important observation is that deep learning can learn the discriminative patterns from raw materials directly in a task-dependent manner. Therefore, the representations obtained by deep learning outperform hand-crafted features significantly. However, those patterns are often learned from super-class labels due to a limited availability of fine-grained labels, while fine-grained patterns are desired in many real-world applications such as visual search in online shopping. To mitigate the challenge, we propose an algorithm to learn the fine-grained patterns sufficiently when only super-class labels are available. The effectiveness of our method can be guaranteed with the theoretical analysis. Extensive experiments on real-world data sets demonstrate that the proposed method can significantly improve the performance on target tasks corresponding to fine-grained classes, when only super-class information is available for training

Intra-Modal Proxy Learning for Zero-Shot Visual Categorization with CLIP

Vision-language pre-training methods, e.g., CLIP, demonstrate an impressive zero-shot performance on visual categorizations with the class proxy from the text embedding of the class name. However, the modality gap between the text and vision space can result in a sub-optimal performance. We theoretically show that the gap cannot be reduced sufficiently by minimizing the contrastive loss in CLIP and the optimal proxy for vision tasks may reside only in the vision space. Therefore, given unlabeled target vision data, we propose to learn the vision proxy directly with the help from the text proxy for zero-shot transfer. Moreover, according to our theoretical analysis, strategies are developed to further refine the pseudo label obtained by the text proxy to facilitate the intra-modal proxy learning (InMaP) for vision. Experiments on extensive downstream tasks confirm the effectiveness and efficiency of our proposal. Concretely, InMaP can obtain the vision proxy within one minute on a single GPU while improving the zero-shot accuracy from $77.02\%$ to $80.21\%$ on ImageNet with ViT-L/14@336 pre-trained by CLIP. Code is available at \url{https://github.com/idstcv/InMaP}.Comment: accepted by NeurIPS'2

Scalable Mask Annotation for Video Text Spotting

Video text spotting refers to localizing, recognizing, and tracking textual elements such as captions, logos, license plates, signs, and other forms of text within consecutive video frames. However, current datasets available for this task rely on quadrilateral ground truth annotations, which may result in including excessive background content and inaccurate text boundaries. Furthermore, methods trained on these datasets often produce prediction results in the form of quadrilateral boxes, which limits their ability to handle complex scenarios such as dense or curved text. To address these issues, we propose a scalable mask annotation pipeline called SAMText for video text spotting. SAMText leverages the SAM model to generate mask annotations for scene text images or video frames at scale. Using SAMText, we have created a large-scale dataset, SAMText-9M, that contains over 2,400 video clips sourced from existing datasets and over 9 million mask annotations. We have also conducted a thorough statistical analysis of the generated masks and their quality, identifying several research topics that could be further explored based on this dataset. The code and dataset will be released at \url{https://github.com/ViTAE-Transformer/SAMText}.Comment: Technical report. Work in progres

The Research of Design Based on Social Commerce

Based on previous design theories which focus only on artifacts, we study the factors of social commerce design with application environment and human capabilities. By comparing social commerce design model and information model, we develop a new social commerce design model, further exploring user requirements after shopping, including the exploration of brand community, sharing offline social shopping experience and the improvement of user social skills. According to the new model, we revealed the common features of social commerce design, including the individual, conversation, community, commerce and management levels. Besides, this paper pointed out social commerce design research problems in future

Development and evaluation of the first high-throughput SNP array for common carp (Cyprinus carpio)

BACKGROUND: A large number of single nucleotide polymorphisms (SNPs) have been identified in common carp (Cyprinus carpio) but, as yet, no high-throughput genotyping platform is available for this species. C. carpio is an important aquaculture species that accounts for nearly 14% of freshwater aquaculture production worldwide. We have developed an array for C. carpio with 250,000 SNPs and evaluated its performance using samples from various strains of C. carpio. RESULTS: The SNPs used on the array were selected from two resources: the transcribed sequences from RNA-seq data of four strains of C. carpio, and the genome re-sequencing data of five strains of C. carpio. The 250,000 SNPs on the resulting array are distributed evenly across the reference C.carpio genome with an average spacing of 6.6 kb. To evaluate the SNP array, 1,072 C. carpio samples were collected and tested. Of the 250,000 SNPs on the array, 185,150 (74.06%) were found to be polymorphic sites. Genotyping accuracy was checked using genotyping data from a group of full-siblings and their parents, and over 99.8% of the qualified SNPs were found to be reliable. Analysis of the linkage disequilibrium on all samples and on three domestic C.carpio strains revealed that the latter had the longer haplotype blocks. We also evaluated our SNP array on 80 samples from eight species related to C. carpio, with from 53,526 to 71,984 polymorphic SNPs. An identity by state analysis divided all the samples into three clusters; most of the C. carpio strains formed the largest cluster. CONCLUSIONS: The Carp SNP array described here is the first high-throughput genotyping platform for C. carpio. Our evaluation of this array indicates that it will be valuable for farmed carp and for genetic and population biology studies in C. carpio and related species

Molecular cloning and expression analysis of the MaASR1 gene in banana and functional characterization under salt stress

Background: Abscisic acid (ABA)-, stress- and ripening-induced protein (ASR) is plant-specific hydrophilic transcriptional regulators involved in sucrose stress and wounding in banana. However, it is not known whether banana ASR genes confer salt stress tolerance. The contexts of the studywas to analysis the sequence characterization of banana ASR1, and identify its expression patterns and function under salt stress using quantitative real-time PCR (qPCR) and overexpression in Arabidopsis . The purpose was to evaluate the role of banana ASR1 to salt stress tolerance employed by plants. Results: A full-length cDNA isolated from banana fruitwas named MaASR1, and it had a 432 bp open reading frame (ORF) encoding 143 amino acids. MaASR1 was preferential expression in roots and leaves compared to low expression in fruits, rhizomes and flowers. Under salt stress, the expression of MaASR1 quickly increased and highest expression level was detected in roots and leaves at 4 h, and then gradually decreased. These results suggested that MaASR1 expression was induced under salt stress. MaASR1 protein was localized in the nucleus and plasma membrane. MaASR1 was transformed to Arabidopsis and verified by southern and northern analysis, transgenic lines L14 and L38 integrated one and two copies of MaASR1, respectively, while overexpression in transgenic lines provided evidence for the role of MaASR1 to salt stress tolerance. Conclusions: This study demonstrated that overexpression of MaASR1 in Arabidopsis confers salt stress tolerance by reducing the expression of ABA/stress-responsive genes, but does not affect the expression of the ABA-independent pathway and biosynthesis pathway genes

Efficient regeneration and genetic transformation platform applicable to five Musa varieties

Background: Banana ( Musa spp.) is an important staple food, economic crop, and nutritional fruit worldwide. Conventional breeding has been seriously hampered by their long generation time, polyploidy, and sterility of most cultivated varieties. Establishment of an efficient regeneration and transformation system for banana is critical to its genetic improvement and functional genomics. Results: In this study, a vigorous and repeatable transformation systemfor banana using direct organogenesiswas developed. The greatest number of shoots per explant for all five Musa varieties was obtained using Murashige and Skoog medium supplemented with 8.9 \u3bcM benzylaminopurine and 9.1 \u3bcM thidiazuron. One immature male flower could regenerate 380\u2013456, 310\u2013372, 200\u2013240, 130\u2013156, and 100\u2013130 well-developed shoots in only 240\u2013270 d for Gongjiao, Red banana, Rose banana, Baxi, and Xinglongnaijiao, respectively. Longitudinal sections of buds were transformed through particle bombardment combined with Agrobacterium -mediated transformation using a promoterless \u3b2-glucuronidase (GUS) reporter gene; the highest transformation efficiency was 9.81% in regenerated Gongjiao plantlets in an optimized selection medium. Transgenic plants were confirmed by a histochemical assay of GUS, polymerase chain reaction, and Southern blot. Conclusions: Our robust transformation platform successfully generated hundreds of transgenic plants. Such a platform will facilitate molecular breeding and functional genomics of banana