Unifying knowledge iterative dissemination and relational reconstruction network for image–text matching

Image–text matching is a crucial branch in multimedia retrieval which relies on learning inter-modal correspondences. Most existing methods focus on global or local correspondence and fail to explore fine-grained global–local alignment. Moreover, the issue of how to infer more accurate similarity scores remains unresolved. In this study, we propose a novel unifying knowledge iterative dissemination and relational reconstruction (KIDRR) network for image–text matching. Particularly, the knowledge graph iterative dissemination module is designed to iteratively broadcast global semantic knowledge, enabling relevant nodes to be associated, resulting in fine-grained intra-modal correlations and features. Hence, vector-based similarity representations are learned from multiple perspectives to model multi-level alignments comprehensively. The relation graph reconstruction module is further developed to enhance cross-modal correspondences by constructing similarity relation graphs and adaptively reconstructing them. We conducted experiments on the datasets Flickr30K and MSCOCO, which have 31,783 and 123,287 images, respectively. Experiments show that KIDRR achieves improvements of nearly 2.2% and 1.6% relative to Recall@1 on Flicr30K and MSCOCO, respectively, compared to the current state-of-the-art baselines.     

Adaptive multi-feature fusion via cross-entropy normalization for effective image retrieval

Multi-feature fusion has achieved gratifying performance in image retrieval. However, some existing fusion mechanisms would unfortunately make the result worse than expected due to the domain and visual diversity of images. As a result, a burning problem for applying feature fusion mechanism is how to figure out and improve the complementarity of multi-level heterogeneous features. To this end, this paper proposes an adaptive multi-feature fusion method via cross-entropy normalization for effective image retrieval. First, various low-level features (e.g., SIFT) and high-level semantic features based on deep learning are extracted. Under each level of feature representation, the initial similarity scores of the query image w.r.t. the target dataset are calculated. Second, we use an independent reference dataset to approximate the tail of the attained initial similarity score ranking curve by cross-entropy normalization. Then the area under the ranking curve is calculated as the indicator of the merit of corresponding feature (i.e., a smaller area indicates a more suitable feature.). Finally, fusion weights of each feature are assigned adaptively by the statistically elaborated areas. Extensive experiments on three public benchmark datasets have demonstrated that the proposed method can achieve superior performance compared with the existing methods, improving the metrics mAP by relatively 1.04% (for Holidays), 1.22% (for Oxf5k) and the N-S by relatively 0.04 (for UKbench), respectively.     

Accelerate adversarial training with loss guided propagation for robust image classification

Adversarial training is effective to train robust image classification models. To improve the robustness, existing approaches often use many propagations to generate adversarial examples, which have high time consumption. In this work, we propose an efficient adversarial training method with loss guided propagation (ATLGP) to accelerate the adversarial training process. ATLGP takes the loss value of generated adversarial examples as guidance to control the number of propagations for each training instance at different training stages, which decreases the computation while keeping the strength of generated adversarial examples. In this way, our method can achieve comparable robustness with less time than traditional training methods. It also has good generalization ability and can be easily combined with other efficient training methods. We conduct comprehensive experiments on CIFAR10 and MNIST, the standard datasets for several benchmarks. The experimental results show that ATLGP reduces 30% to 60% training time compared with other baseline methods while achieving similar robustness against various adversarial attacks. The combination of ATLGP and ATTA (an efficient adversarial training method) achieves superior acceleration potential when robustness meets high requirements. The statistical propagation in different training stages and ablation studies prove the effectiveness of applying loss guided propagation for each training instance. The acceleration technique can more easily extend adversarial training methods to large-scale datasets and more diverse model architectures such as vision transformers.     

匀速直线运动模糊图像的退化模型研究

本文分析并建立了匀速直线运动模糊图像的退化数学模型,研究了点扩展函数的计算方法.结合汽车碰撞试验,对匀速直线运动造成的图像模糊进行了模拟和恢复实验,取得了令人满意的效果.     

利用空间投影法实现遥感图像的几何精纠正

遥感图像必须经过校正才能进一步应用,本文采用空间投影法对缺少控制点的遥感图像进行几何精纠正.文章介绍了遥感图像几何精纠正原理及空间投影理论,详细阐述了整个几何精纠正的算法过程,并给出实验结果.     

消解与重构——虚拟环境对个体自我认同的影响

网络社区的形成为个体提供了更为广泛的虚拟生存空间。从一系列媒介事件对虚拟环境中生存的个体的价值观、历史与文化、情感与身份进行探讨,可见媒介环境正不断地消解并重构真实环境与个体,当代人正面临着真实判断能力丧失、个体情感媒介化、价值观同化、历史断裂、文化重组的危机。     

金牛厂铅锌矿遥感线性构造解译及找矿应用

在地形复杂地区利用高分辨率SPOT影像进行线性构造解译.采用去相关拉伸、波段相关性等技术增强图像,计算图像标准差、方差等统计参数,利用相关参数进行遥感影像融合效果的分析与评价.以目视解译和机助解译相结合的方法获取线性构造信息,并对金牛厂矿区线性构造信息进行半定量统计.利用统计结果,并结合矿区地质资料,对线性构造分布规律进行分析与评价.研究表明,金牛厂矿区的NE向和NNE向线性构造更为发育,为金牛厂矿区控矿构造,圈定三个成矿有利构造区.并用坑探方法进行证明,取得了很好的找矿效果.     

浅析托尼·莫里森小说《爱》中的手意象

托尼·莫里森在2003年出版的第8部小说《爱》延续了其一贯探索美国黑人的历史和文化的写作风格,以“爱”为主题,以已故的男主人公比尔·柯西对周围的人产生的影响为线索,讲述了一个爱恨交织的复杂故事。象征是小说中一种强有力的表现手法,在这部小说中,手意象是被赋予特殊含义的。莫里森通过描写不同侧面的手意象生动地刻画了人物性格、经历、矛盾和关系,在集中体现了黑人文化传统的同时,也揭示了爱的主题。     

Epss在教师教育技术培训中的技术实现

论文从高等院校教师教育技术培训的现状出发,结合实际技术,尝试研究一个以培训高等院校教师教育技术为案例的电子绩效网络支持系统。整个系统包括学习信息库,训练系统,专家指导系统,效能工具和学习帮助系统五部分,希望通过此系统可以较大幅度的提高培训绩效,并为高等院校教师教育技术培训提供新的思路和方向。     

Participation,financial support and the marginal student

This paper examines differences between the decision-making of marginal and nonmarginal students about participation in higher education (HE). We distinguish between two kinds of marginality: being ‘borderline’ on account of prior achievements in school and being ‘unsure’ after taking prior achievement into account. We identify a significant minority of students in their final year of schooling who are unsure about participation in higher education even though they have prior school achievements typical of entrants to HE. Being ‘unsure’ is found to be unrelated to socioeconomic background, but it is associated with significantly different attitudes towards the potential benefits and risks of participation, different levels of knowledge about financial support that is available and different approaches to information search about participation in HE.