Search

Your search keyword '"Liang, Jing"' showing total 9,720 results
Start Over Author "Liang, Jing"
9,720 results on '"Liang, Jing"'

239. Analyzing the expected values of neighborhood degree-based topological indices in random cyclooctane chains

Author

Liang Jing, Shamaila Yousaf, Saira Farhad, Fairouz Tchier, and Adnan Aslam

Subjects
chemical graph theory, topological indices, cyclooctane chains, expected values, Randic index, Chemistry, QD1-999
Abstract

Cyclooctane is classified as a cycloalkane, characterized by the chemical formula C8H16. It consists of a closed ring structure composed of eight carbon atoms and sixteen hydrogen atoms. A cyclooctane chain typically refers to a series of cyclooctane molecules linked together. Cyclooctane and its derivatives find various applications in chemistry, materials science, and industry. Topological indices are numerical values associated with the molecular graph of a chemical compound, predicting certain physical or chemical properties. In this study, we calculated the expected values of degree-based and neighborhood degree-based topological descriptors for random cyclooctane chains. A comparison of these topological indices’ expected values is presented at the end.

Published
2024
Full Text
View/download PDF

240. Image-Goal Navigation in Complex Environments via Modular Learning

Author

Wu, Qiaoyun, Wang, Jun, Liang, Jing, Gong, Xiaoxi, and Manocha, Dinesh

Subjects
Computer Science - Robotics
Abstract

We present a novel approach for image-goal navigation, where an agent navigates with a goal image rather than accurate target information, which is more challenging. Our goal is to decouple the learning of navigation goal planning, collision avoidance, and navigation ending prediction, which enables more concentrated learning of each part. This is realized by four different modules. The first module maintains an obstacle map during robot navigation. The second predicts a long-term goal on the real-time map periodically, which can thus convert an image-goal navigation task to several point-goal navigation tasks. To achieve these point-goal navigation tasks, the third module plans collision-free command sets for navigating to these long-term goals. The final module stops the robot properly near the goal image. The four modules are designed or maintained separately, which helps cut down the search time during navigation and improves the generalization to previously unseen real scenes. We evaluate the method in both a simulator and in the real world with a mobile robot. The results in real complex environments show that our method attains at least a $17\%$ increase in navigation success rate and a $23\%$ decrease in navigation collision rate over some state-of-the-art models., Comment: 9 pages

Published
2021

241. Visible Watermark Removal via Self-calibrated Localization and Background Refinement

Author

Liang, Jing, Niu, Li, Guo, Fengjun, Long, Teng, and Zhang, Liqing

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, I.4
Abstract

Superimposing visible watermarks on images provides a powerful weapon to cope with the copyright issue. Watermark removal techniques, which can strengthen the robustness of visible watermarks in an adversarial way, have attracted increasing research interest. Modern watermark removal methods perform watermark localization and background restoration simultaneously, which could be viewed as a multi-task learning problem. However, existing approaches suffer from incomplete detected watermark and degraded texture quality of restored background. Therefore, we design a two-stage multi-task network to address the above issues. The coarse stage consists of a watermark branch and a background branch, in which the watermark branch self-calibrates the roughly estimated mask and passes the calibrated mask to background branch to reconstruct the watermarked area. In the refinement stage, we integrate multi-level features to improve the texture quality of watermarked area. Extensive experiments on two datasets demonstrate the effectiveness of our proposed method.

Published
2021
Full Text
View/download PDF

242. Making Images Real Again: A Comprehensive Survey on Deep Image Composition

Author

Niu, Li, Cong, Wenyan, Liu, Liu, Hong, Yan, Zhang, Bo, Liang, Jing, and Zhang, Liqing

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

As a common image editing operation, image composition aims to combine the foreground from one image and another background image, resulting in a composite image. However, there are many issues that could make the composite images unrealistic. These issues can be summarized as the inconsistency between foreground and background, which includes appearance inconsistency (e.g., incompatible illumination), geometry inconsistency (e.g., unreasonable size), and semantic inconsistency (e.g., mismatched semantic context). Image composition task could be decomposed into multiple sub-tasks, in which each sub-task targets at one or more issues. Specifically, object placement aims to find reasonable scale, location, and shape for the foreground. Image blending aims to address the unnatural boundary between foreground and background. Image harmonization aims to adjust the illumination statistics of foreground. Shadow generation aims to generate plausible shadow for the foreground. These sub-tasks can be executed sequentially or parallelly to acquire realistic composite images. To the best of our knowledge, there is no previous survey on image composition. In this paper, we conduct comprehensive survey over the sub-tasks and combinatorial task of image composition. For each one, we summarize the existing methods, available datasets, and common evaluation metrics. Datasets and codes for image composition are summarized at https://github.com/bcmi/Awesome-Image-Composition. We have also contributed the first image composition toolbox: libcom https://github.com/bcmi/libcom, which assembles 10+ image composition related functions (e.g., image blending, image harmonization, object placement, shadow generation, generative composition). The ultimate goal of this toolbox is solving all the problems related to image composition with simple `import libcom'.

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results