Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and Retention

Scene Graph Generation (SGG) offers a structured representation critical in many computer vision applications. Traditional SGG approaches, however, are limited by a closed-set assumption, restricting their ability to recognize only predefined object and relation categories. To overcome this, we categorize SGG scenarios into four distinct settings based on the node and edge: Closed-set SGG, Open Vocabulary (object) Detection-based SGG (OvD-SGG), Open Vocabulary Relation-based SGG (OvR-SGG), and Open Vocabulary Detection + Relation-based SGG (OvD+R-SGG). While object-centric open vocabulary SGG has been studied recently, the more challenging problem of relation-involved open-vocabulary SGG remains relatively unexplored. To fill this gap, we propose a unified framework named OvSGTR towards fully open vocabulary SGG from a holistic view. The proposed framework is an end-toend transformer architecture, which learns a visual-concept alignment for both nodes and edges, enabling the model to recognize unseen categories. For the more challenging settings of relation-involved open vocabulary SGG, the proposed approach integrates relation-aware pre-training utilizing image-caption data and retains visual-concept alignment through knowledge distillation. Comprehensive experimental results on the Visual Genome benchmark demonstrate the effectiveness and superiority of the proposed framework.Comment: 10 pages, 4 figures, 6 table

Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the size of training data for RS image generation than for natural image generation. In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data. Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. We theoretically and empirically demonstrate the effectiveness and versatility of our methods. Extensive experiments on three RS datasets and two natural datasets show that our methods outperform the well-established models on RS image generation tasks. The source code is available at https://github.com/rootSue/Causal-RSGAN

A Unified GAN Framework Regarding Manifold Alignment for Remote Sensing Images Generation

Generative Adversarial Networks (GANs) and their variants have achieved remarkable success on natural images. However, their performance degrades when applied to remote sensing (RS) images, and the discriminator often suffers from the overfitting problem. In this paper, we examine the differences between natural and RS images and find that the intrinsic dimensions of RS images are much lower than those of natural images. As the discriminator is more susceptible to overfitting on data with lower intrinsic dimension, it focuses excessively on local characteristics of RS training data and disregards the overall structure of the distribution, leading to a faulty generation model. In respond, we propose a novel approach that leverages the real data manifold to constrain the discriminator and enhance the model performance. Specifically, we introduce a learnable information-theoretic measure to capture the real data manifold. Building upon this measure, we propose manifold alignment regularization, which mitigates the discriminator's overfitting and improves the quality of generated samples. Moreover, we establish a unified GAN framework for manifold alignment, applicable to both supervised and unsupervised RS image generation tasks

Effect of Maize (Zea mays L.) Plant-Type on Yield and Photosynthetic Characters of Sweet Potato (Ipomoea balatas L.) in Intercropping System

Sweet potato/maize relay-cropping mode is considered as the main farming practices of dry land in Southwest China. Although relay-cropping would cause the reduction of fresh tuber yield, it still remained unclear that the reason was shade resulted from maize or genotype of sweet potato. The present work aims at exploring the effects of maize (Zea mays L.) plant-type on photosynthetic physiology and yield of sweet potato (Ipomoea balatas L.) in relay-cropping system. Besides, three plant-types maize cultivars including compact, semi-compact and expanded type were used for relay-cropping with different sweet potato cultivars (‘Yushu-2’, ‘Yushu-6’ and ‘Nanshu-88’) in field. The results showed that the photosynthetically active radiation (PAR) was declined with the increase of expansion of maize plant-type, which decreased by 77.5%, 80.1% and 82.1% respectively. When relay-cropped with extended maize, the yield reduction rate of sweet potato was the highest (67%). The shade-resistance of different genotype of sweet potatoes was different, and the yield reduction rate of ‘Yushu-2’ was the lowest (37.01%). Through conducting correlations analysis, it showed that fresh tuber yield had significant positive correlation with Effective Quantum Yield (Y(II)) and significant negative correlation with Non Photochemical Quenching Coefficient (NPQ). In terms of ‘Yushu-2’, the proportion of heat dissipation was the lowest, and its light quantum efficiency was higher than others. As a result, its reduction rate of yield was lower than the other two. We suggested that compact maize cultivar relay-cropping with strong shade-resistance sweet potato cultivar should be mainly applied in practice of sweet potato

How to increase productivity of the copepod Acartia tonsa (Dana): effects of population density and food concentration

In this study, we analysed the effect of population density and food concentration on the fecundity of a Mediterranean strain of Acartia tonsa to maximize egg production. During 4-day feeding experiments, egg hatching success and faecal pellet production were also followed. The algae Rhinomonas reticulata was supplied at different concentrations corresponding to 250, 500, 1000, 1500, 2000 and 3000 μg C L−1 day−1 at the following adult copepod density: 40, 80 and 160 ind. L−1. Our results show a positive relationship between algal concentration and egg production under all experimental conditions confirming that the quantity of food strongly limits A. tonsa fecundity. Maximum egg production (57 eggs per female) was reached at the lowest density and at the maximum food concentration. Percentage of egg hatching success was not dependent on the quantity of food used. At the same food concentration, an increase in population density from 40 to 80 ind. L−1 induced an increase in faecal pellet production per couple which did not correspond to an increase in egg production, suggesting that higher energetic costs were shifted to swimming activity. Productivity of the A. tonsa Mediterranean strain is mainly limited by the quantity of food rather than by crowding conditions

Unbiased Image Synthesis via Manifold-Driven Sampling in Diffusion Models

Diffusion models are a potent class of generative models capable of producing high-quality images. However, they can face challenges related to data bias, favoring specific modes of data, especially when the training data does not accurately represent the true data distribution and exhibits skewed or imbalanced patterns. For instance, the CelebA dataset contains more female images than male images, leading to biased generation results and impacting downstream applications. To address this issue, we propose a novel method that leverages manifold guidance to mitigate data bias in diffusion models. Our key idea is to estimate the manifold of the training data using an unsupervised approach, and then use it to guide the sampling process of diffusion models. This encourages the generated images to be uniformly distributed on the data manifold without altering the model architecture or necessitating labels or retraining. Theoretical analysis and empirical evidence demonstrate the effectiveness of our method in improving the quality and unbiasedness of image generation compared to standard diffusion models

Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective

Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from such data. Accordingly, graph neural networks (GNNs) indeed achieve impressive success in various fields. Revisiting the GNN learning paradigms, we discover that the relationship between human expertise and the knowledge modeled by GNNs still confuses researchers. To this end, we introduce motivating experiments and derive an empirical observation that the human expertise is gradually learned by the GNNs in general domains. By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance. By exploring the intrinsic mechanism behind such observations, we elaborate the Structural Causal Model for the graph representation learning paradigm. Following the theoretical guidance, we innovatively introduce the auxiliary causal logic learning paradigm to improve the model to learn the expertise logic causally related to the graph representation learning task. In practice, the counterfactual technique is further performed to tackle the insufficient training issue during optimization. Plentiful experiments on the crafted and real-world domains support the consistent effectiveness of the proposed method

Genetic diversity and population structure of Sepiella japonica (Mollusca: Cephalopoda: Decapoda) inferred by 16S rDNA variations

In order to describe the genetic diversity and phylogenetic relationship of five populations of cuttlefish (Sepiella japonica) along with China's coasts, partial 16S rDNA (510 bp in length) was amplified from 110 individuals. The five populations of cuttlefish inhabit Yellow Sea, East China Sea and South China Sea. In total, six haplotypes were identified and formed only one clade. Among the six haplotypes, one was shared by all populations, three appeared only in a single population, two appeared in two or three populations. Pair-wise FST were not proportional to the geographical distances. Haplotype diversity and nucleotide diversity were low, 0.3866 ± 0.067 and 0.00120 ± 0.00081 respectively. Among the five populations, Zhoushan population exhibited the highest genetic diversity which was suggested as the better select of germplasm resources for the reproduction and releasing of S. japonica

Regulatory and functional divergence among members of Ibβfruct2, a sweet potato vacuolar invertase gene controlling starch and glucose content

Sweet potato [Ipomoea batatas (L.) Lam.] is an important food and industrial crop. Its storage root is rich in starch, which is present in the form of granules and represents the principal storage carbohydrate in plants. Starch content is an important trait of sweet potato controlling the quality and yield of industrial products. Vacuolar invertase encoding gene Ibβfruct2 was supposed to be a key regulator of starch content in sweet potato, but its function and regulation were unclear. In this study, three Ibβfruct2 gene members were detected. Their promoters displayed differences in sequence, activity, and cis-regulatory elements and might interact with different transcription factors, indicating that the three Ibβfruct2 family members are governed by different regulatory mechanisms at the transcription level. Among them, we found that only Ibβfruct2-1 show a high expression level and promoter activity, and encodes a protein with invertase activity, and the conserved domains and three conserved motifs NDPNG, RDP, and WEC are critical to this activity. Only two and six amino acid residue variations were detected in sequences of proteins encoded by Ibβfruct2-2 and Ibβfruct2-3, respectively, compared with Ibβfruct2-1; although not within key motifs, these variations affected protein structure and affinities for the catalytic substrate, resulting in functional deficiency and low activity. Heterologous expression of Ibβfruct2-1 in Arabidopsis decreased starch content but increased glucose content in leaves, indicating Ibβfruct2-1 was a negative regulator of starch content. These findings represent an important advance in understanding the regulatory and functional divergence among duplicated genes in sweet potato, and provide critical information for functional studies and utilization of these genes in genetic improvement

Phylogenetic and molecular characterization of coxsackievirus A24 variant isolates from a 2010 acute hemorrhagic conjunctivitis outbreak in Guangdong, China

<p>Abstract</p> <p>Background</p> <p>Acute hemorrhagic conjunctivitis is a common disease in China. As a notifiable disease, cases are registered by ophthalmologists on the AHC surveillance system. An AHC outbreak caused by CA24v was observed in Guangdong Province in 2007 by the National Disease Supervision Information Management System. Three years later, a larger outbreak occurred in Guangdong during the August-October period (2010). To characterize the outbreak and compare the genetic diversity of CA24v, which was determined to be the cause of the outbreak, the epidemiology and the molecular characterization of CA24v were analyzed in this study.</p> <p>Results</p> <p>A total of 69,635 cases were reported in the outbreak. 73.5% of index cases originated from students, children in kindergarten and factory workers, with the ≦ 9 age group at the highest risk. The male to female ratio was 1.84:1 among 0-19 years. 56 conjunctival swabs were collected to identify the causative agent from five cities with the AHC outbreak. 30 virus strains were isolated, and two of the genomes had the highest identity values (95.8%) with CA24v genomes. Four CA24v genotypes were identified by phylogenetic analysis for the VP1 and 3C regions. CA24v which caused the outbreak belonged to genotype IV. Furthermore, full nucleotide sequences for four representative isolates in 2010 and 2007 were determined and compared. 20 aa mutations, two nt insertions and one nt deletion were observed in the open reading frame, with 5'- and 3'- UTR respectively between them.</p> <p>Conclusions</p> <p>CA24v was determined to be the pathogen causing the outbreak and belongs to genotype IV. VP1 is more informative than 3C^Profor describing molecular epidemiology and we hypothesize that accumulative mutations may have promoted the outbreak.</p