Ying LiNanjing Normal University · School of Computer and Electronic Information/ School of Artificial Intelligence
Ying Li
Doctor of Engineering
About
21
Publications
2,241
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140
Citations
Introduction
Ying Li is currently an Assistant Professor in the School of Computer and Electronic Information/ School of Artificial Intelligence, Nanjing Normal University. Her research interests are computer vision and pattern recognition.
[Recent News] I will serve as a Leading Guest Editor for Pattern Recognition (PR). The Important Dates and submission details are released here: https://www.journals.elsevier.com/pattern-recognition/call-for-papers/fine-grained-object-retrieval-matching-and-ranking (Open)
Additional affiliations
June 2019 - present
Nanjing Normal University
Position
- Professor (Assistant)
August 2018 - May 2019
Noah’s Ark Lab of Huawei
Position
- Engineer
Description
- Computer Vision
March 2014 - August 2014
Beijing Institue of Electronics Technology and Application
Position
- Research Assistant
Description
- Image Forensics
Education
December 2015 - December 2017
September 2014 - March 2019
September 2012 - August 2014
Publications
Publications (21)
Recent days have seen significant improvements in multi-modal learning made by Vision-Language Pre-training (VLP) models. However, most of them employ the coarse-grained global alignment to overcome semantic gap for generating common representations, which makes them inadequate to capture intrinsic semantic correlations in image-text retrieval and...
Image-Text matching plays an important role in solving the problem of cross-modal information processing. Since there are nonnegligible semantic differences between heterogenous pairwise data, a crucial challenge is how to learn a unified representation. Existing methods mainly rely on the alignment between regional image features and corresponding...
Recently, matrix factorization-based hashing has gained wide attention because of its strong subspace learning ability and high search efficiency. However, some problems need to be further addressed. First, uniform hash codes can be generated by collective matrix factorization, but they often cause serious loss, degrading the quality of hash codes....
Significant development of video person re-identification has been witnessed in recent years with deep learning technologies. Due to the complexity of human pose changes and the similarity between different individuals, learning discriminative features is still a challenging part of the video person re-identification task. To get rid of the effects...
Cross-media hashing, which encodes data points from different modalities into a common Hamming space, has been successfully applied to solve large-scale multimedia retrieval issue due to storage efficiency and search effectiveness. Recently, matrix factorization based hashing methods have drawn considerable attention for their promising search accu...
Conventionally, the similarity between two images is measured by the easy-calculating Euclidean distance between their corresponding image feature representations for image retrieval. However, this kind of direct similarity measurement ignores the local geometry structure of the intrinsic data manifold, which is not discriminative enough for robust...
Person Re-identification (ReID) has witnessed remarkable improvements in the past couple of years. However, its applications in real-world scenarios are limited by the disparity among different cameras and datasets. In general, it remains challenging to generalize ReID algorithms from one domain to another, especially when the target domain is unkn...
The existing semantic enrichment process approaches which can produce semantic trajectories, are generally time consuming. In this paper, we propose a semantic enrichment process framework for spatiotemporal trajectories in geospatial environment. It can derive new semantic trajectories through the three phases: pre-annotated semantic trajectories...
Convolutional Neural Network (CNN) has brought significant improvements for various multimedia tasks. In contrast, image retrieval has not yet benefited as much since no training database is available. In this paper, we propose an unsupervised weighting scheme for pre-trained CNN models to adaptively emphasize image center. Different from the gener...
Various kinds of features prove to be effective for content-based image retrieval. However, due to the diversity of image contents, a descriptor may achieve impressive performance on specific images while becoming invalid on others. Although some efforts have been made to combine features as complementary counterparts, proper weighting scheme is st...
Image reranking is an effective post-processing step to adjust the similarity order in image retrieval. As key components of initialized ranking lists, top-ranked neighborhoods of a given query usually play important roles in constructing dissimilarity measure. However, the number of pertinent candidates varies with respect to different queries. Th...
Similarity measurement is an essential component in image retrieval systems. While previous work is focused on generic distance estimation, this paper investigates the problem of similarity estimation within a local neighborhood defined in the original feature space. Specifically, our method is characterized in two aspects, i.e., “local” and “resid...
Hashing techniques have been widely adopted for cross-modal retrieval due to its low storage cost and fast query speed. Most existing cross-modal hashing methods aim to map heterogeneous data into the common low-dimensional hamming space and then threshold to obtain binary codes by relaxing the discrete constraint. However, this independent relaxat...
The Convolutional Neural Networks (CNNs) have achieved breakthroughs on several image retrieval benchmarks. Most previous works re-formulate CNNs as global feature extractors used for linear scan. This paper proposes a Multi-layer Orderless Fusion (MOF) approach to integrate the activations of CNN in the Bag-of-Words (BoW) framework. Specifically,...
In this paper, a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering. Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do var...