A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201

Motion-state Alignment for Video Semantic Segmentation

In recent years, video semantic segmentation has made great progress with advanced deep neural networks. However, there still exist two main challenges \ie, information inconsistency and computation cost. To deal with the two difficulties, we propose a novel motion-state alignment framework for video semantic segmentation to keep both motion and state consistency. In the framework, we first construct a motion alignment branch armed with an efficient decoupled transformer to capture dynamic semantics, guaranteeing region-level temporal consistency. Then, a state alignment branch composed of a stage transformer is designed to enrich feature spaces for the current frame to extract static semantics and achieve pixel-level state consistency. Next, by a semantic assignment mechanism, the region descriptor of each semantic category is gained from dynamic semantics and linked with pixel descriptors from static semantics. Benefiting from the alignment of these two kinds of effective information, the proposed method picks up dynamic and static semantics in a targeted way, so that video semantic regions are consistently segmented to obtain precise locations with low computational complexity. Extensive experiments on Cityscapes and CamVid datasets show that the proposed approach outperforms state-of-the-art methods and validates the effectiveness of the motion-state alignment framework.Comment: Accepted by CVPR Workshops 202

Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation

Recently, instance segmentation has made great progress with the rapid development of deep neural networks. However, there still exist two main challenges including discovering indistinguishable objects and modeling the relationship between instances. To deal with these difficulties, we propose a novel object mining framework for instance segmentation. In this framework, we first introduce the semantics perceiving subnetwork to capture pixels that may belong to an obvious instance from the bottom up. Then, we propose an object excavating mechanism to discover indistinguishable objects. In the mechanism, preliminary perceived semantics are regarded as original instances with classifications and locations, and then indistinguishable objects around these original instances are mined, which ensures that hard objects are fully excavated. Next, an instance purifying strategy is put forward to model the relationship between instances, which pulls the similar instances close and pushes away different instances to keep intra-instance similarity and inter-instance discrimination. In this manner, the same objects are combined as the one instance and different objects are distinguished as independent instances. Extensive experiments on the COCO dataset show that the proposed approach outperforms state-of-the-art methods, which validates the effectiveness of the proposed object mining framework.Comment: Accepted by CVPR Workshops 202

Text2Street: Controllable Text-to-image Generation for Street Views

Text-to-image generation has made remarkable progress with the emergence of diffusion models. However, it is still a difficult task to generate images for street views based on text, mainly because the road topology of street scenes is complex, the traffic status is diverse and the weather condition is various, which makes conventional text-to-image models difficult to deal with. To address these challenges, we propose a novel controllable text-to-image framework, named \textbf{Text2Street}. In the framework, we first introduce the lane-aware road topology generator, which achieves text-to-map generation with the accurate road structure and lane lines armed with the counting adapter, realizing the controllable road topology generation. Then, the position-based object layout generator is proposed to obtain text-to-layout generation through an object-level bounding box diffusion strategy, realizing the controllable traffic object layout generation. Finally, the multiple control image generator is designed to integrate the road topology, object layout and weather description to realize controllable street-view image generation. Extensive experiments show that the proposed approach achieves controllable street-view text-to-image generation and validates the effectiveness of the Text2Street framework for street views

Validation of the Chinese version of the exercise empowerment scale among college students (EES-C)

ObjectiveThe escalating prevalence of physical inactivity among college students represents a significant public health challenge, particularly in light of its correlation with detrimental health outcomes. A growing body of evidence underscores the necessity of adhering to recommended levels of regular physical activity to thwart the onset of chronic diseases. One primary aim of school physical education initiatives is to elevate physical activity levels and bolster student motivation toward engaging in physical exercise. Engagement in sports activities has demonstrated efficacy in augmenting students’ motor skills, elevating their self-efficacy, and enhancing cognitive competencies related to physical prowess, while also promoting sustained participation in physical activities. The Exercise Empowerment Scale (EES) has been formulated to quantitatively assess the degree of exercise empowerment. To date, no validated Chinese version of the EES has been reported in the literature. To address this, this present study developed and validated a Chinese version of the EES in a large sample of Chinese college students.MethodsA sample of Chinese college students (n = 885) was analyzed using Rasch analysis to examine the validity of the Exercise Empowerment Scale. In addition, physical activity and psychological resilience were used to investigate the potential associations with exercise empowerment.ResultsThe EES-C was found to be a unidimensional scale, and the distribution of item difficulty was relatively even. The scale had good reliability (individual reliability of 0.87, and item reliability of 0.99). No differential item functioning (DIF) was observed across genders for any of the 13 EES-C items, suggesting equitable and unbiased applicability for both male and female respondents. The five-point scoring method of the EES scale was consistent with the overall distribution of the items and subjects. Exercise empowerment was positively correlated with autonomous physical activity and psychological resilience.ConclusionThe results of the study indicate that the EES-C possesses robust psychometric properties, rendering it suitable for application among Chinese college student populations. The adapted version of the EES-C provides a basis for further exploration of the predictive factors of physical activity in Chinese samples. The generalizability of our findings should be further verified in other populations in the future

Study of imbibition in various geometries using phase field method

Phase field method has been widely utilized to study multiphase flow problems, but has seldom been applied to the study of imbibition. Previous methods used to simulate imbibition, such as moving mesh method, need to specify capillary pressure as a boundary condition a priori, whereas phase field method can calculate capillary pressure automatically for various geometries. Therefore, phase field method would be a versatile tool for the study of imbibition in various geometries. In this paper, phase field method is employed to solve dynamical imbibition problem in various geometries, including straight tube, conical tube and structures in which the topology changes. The variation of the imbibition height with respect to time from phase field simulation is verified with theoretical predictions from Lucas-Washburn law in a straight capillary tube with three gravitational scenarios. In addition, the capillary pressure and velocity field are found to be consistent with Laplace-Young equation and Hagen-Poiseuille equation in various geometries. The applicability and accuracy of the phase field method for the study of imbibition in structures with changing topology are also discussed.Cited as: Xiao, J., Luo, Y., Niu, M., Wang, Q., Wu, J., Liu, X., Xu, J. Study of imbibition in various geometries using phase field method. Capillarity, 2019, 2(4): 57-65, doi: 10.26804/capi.2019.04.0