arXiv subjects

Anan Liu

Publications and source records attributed to Anan Liu.

23 records · Page 2Linked to original sources

arXiv2023

Chest X-ray Image Classification: A Causal Perspective

The chest X-ray (CXR) is one of the most common and easy-to-get medical tests used to diagnose common diseases of the chest. Recently, many deep learning-based methods have been proposed that are capable of effectively classifying CXRs. Even though these techniques have worked quite well, it is difficult to establish whether what these algorithms actually learn is the cause-and-effect link between diseases and their causes or just how to map labels to photos.In this paper, we propose a causal approach to address the CXR classification problem, which constructs a structural causal model (SCM) and uses the backdoor adjustment to select effective visual information for CXR classification. Specially, we design different probability optimization functions to eliminate the influence of confounders on the learning of real causality. Experimental results demonstrate that our proposed method outperforms the open-source NIH ChestX-ray14 in terms of classification performance.

eess.IV↗

arXiv2022

Disentangled Multimodal Representation Learning for Recommendation

Many multimodal recommender systems have been proposed to exploit the rich side information associated with users or items (e.g., user reviews and item images) for learning better user and item representations to improve the recommendation performance. Studies from psychology show that users have individual differences in the utilization of various modalities for organizing information. Therefore, for a certain factor of an item (such as appearance or quality), the features of different modalities are of varying importance to a user. However, existing methods ignore the fact that different modalities contribute differently towards a user's preference on various factors of an item. In light of this, in this paper, we propose a novel Disentangled Multimodal Representation Learning (DMRL) recommendation model, which can capture users' attention to different modalities on each factor in user preference modeling. In particular, we employ a disentangled representation technique to ensure the features of different factors in each modality are independent of each other. A multimodal attention mechanism is then designed to capture users' modality preference for each factor. Based on the estimated weights obtained by the attention mechanism, we make recommendations by combining the preference scores of a user's preferences to each factor of the target item over different modalities. Extensive evaluation on five real-world datasets demonstrate the superiority of our method compared with existing methods.

cs.IR↗

arXiv2022

Ferrimagnetism in stable non-metal covalent organic framework

We synthesized a pure organic non-metal crystalline covalent organic framework TAPA-BTD-COF by bottom-up Schiff base chemical reaction. And this imine-based COF is stable in aerobic condition and room-temperature. We discovered that this TAPA-BTD-COF exhibited strong magneticity in 300 K generating magnetic hysteresis loop in M-H characterization and giant chimol up to 0.028. And we further conducted zero-field cooling and field-cooling measurement of M-T curves. The as-synthesized materials showed a large chi/mol up to 0.028 in 300 K and increasing to 0.037 in 4.0 K with 200 Oe measurement field. The TAPA-BTD-COF 1/chimol~T curve supported its ferrimagnetism, with an intrinsic delta temperature as -33.03 K by extrapolating the 1/chimol~T curve. From the continuously increasing slope of 1/chimol~T, we consider that this TAPA-BTD-COF belongs to ferrimagnetic other than antiferromagnetic materials. And the large chimol value 0.028 at 300 K and 0.037 at 4.0 K also supported this, since common antiferromagnetic materials possess chimol in the range of 10-5 to 10-3 as weak magnetics other than strong magnetic materials such as ferrimagnetics and ferromagnetics. Since this material is purely non-metal organic polymer, the possibility of d-block and f-block metal with unpaired-electron induced magnetism can be excluded. Besides, since the COF does not involve free-radical monomer in the processes of synthesis, we can also exclude the origin of free-radical induced magnetism. According to recent emerging flat-band strong correlated exotic electron property, this unconventional phenomenon may relate to n-type doping on the flat-band locating in the CBM, thus generating highly-localized electron with infinite effective mass and exhibiting strong correlation, which accounts for this non-trivial strong and stable ferrimagneticity at room-temperature and aerobic atmospheric conditions.

cond-mat.mtrl-sci↗

arXiv2021

TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization

Finding tampered regions in images is a hot research topic in machine learning and computer vision. Although many image manipulation location algorithms have been proposed, most of them only focus on the RGB images with different color spaces, and the frequency information that contains the potential tampering clues is often ignored. In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework. Specifically, we first design an adaptive frequency selection module (AFS) to adaptively select the appropriate frequency to mine inconsistent statistics and eliminate the interference of redundant statistics. Then, an adaptive cross-attention fusion module (ACF) is proposed to adaptively fuse the RGB feature and the frequency feature. Finally, the boundary artifact location network (BAL) is designed to locate the boundary artifacts for which the parameters are jointly updated by the outputs of the ACF, and its results are further fed into the decoder. Thus, the parameters of the RGB stream, the frequency stream, and the boundary artifact location network are jointly optimized, and their latent complementary relationships are fully mined. The results of extensive experiments performed on four public benchmarks of the image manipulation localization task, namely, CASIA1.0, COVER, Carvalho, and In-The-Wild, demonstrate that the proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization methods in terms of both MCC and F1.

cs.CV↗

arXiv2020

Zero-Shot Multi-View Indoor Localization via Graph Location Networks

Indoor localization is a fundamental problem in location-based applications. Current approaches to this problem typically rely on Radio Frequency technology, which requires not only supporting infrastructures but human efforts to measure and calibrate the signal. Moreover, data collection for all locations is indispensable in existing methods, which in turn hinders their large-scale deployment. In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization. GLN makes location predictions based on robust location representations extracted from images through message-passing networks. Furthermore, we introduce a novel zero-shot indoor localization setting and tackle it by extending the proposed GLN to a dedicated zero-shot version, which exploits a novel mechanism Map2Vec to train location-aware embeddings and make predictions on novel unseen locations. Our extensive experiments show that the proposed approach outperforms state-of-the-art methods in the standard setting, and achieves promising accuracy even in the zero-shot setting where data for half of the locations are not available. The source code and datasets are publicly available at https://github.com/coldmanck/zero-shot-indoor-localization-release.

cs.CV↗