arXiv subjects

Jingyi Xie

Publications and source records attributed to Jingyi Xie.

24 records · Page 2Linked to original sources

arXiv2023

Knowledge-Enhanced Hierarchical Information Correlation Learning for Multi-Modal Rumor Detection

The explosive growth of rumors with text and images on social media platforms has drawn great attention. Existing studies have made significant contributions to cross-modal information interaction and fusion, but they fail to fully explore hierarchical and complex semantic correlation across different modality content, severely limiting their performance on detecting multi-modal rumor. In this work, we propose a novel knowledge-enhanced hierarchical information correlation learning approach (KhiCL) for multi-modal rumor detection by jointly modeling the basic semantic correlation and high-order knowledge-enhanced entity correlation. Specifically, KhiCL exploits cross-modal joint dictionary to transfer the heterogeneous unimodality features into the common feature space and captures the basic cross-modal semantic consistency and inconsistency by a cross-modal fusion layer. Moreover, considering the description of multi-modal content is narrated around entities, KhiCL extracts visual and textual entities from images and text, and designs a knowledge relevance reasoning strategy to find the shortest semantic relevant path between each pair of entities in external knowledge graph, and absorbs all complementary contextual knowledge of other connected entities in this path for learning knowledge-enhanced entity representations. Furthermore, KhiCL utilizes a signed attention mechanism to model the knowledge-enhanced entity consistency and inconsistency of intra-modality and inter-modality entity pairs by measuring their corresponding semantic relevant distance. Extensive experiments have demonstrated the effectiveness of the proposed method.

cs.CV↗

arXiv2022

Label Noise-Resistant Mean Teaching for Weakly Supervised Fake News Detection

Fake news spreads at an unprecedented speed, reaches global audiences and poses huge risks to users and communities. Most existing fake news detection algorithms focus on building supervised training models on a large amount of manually labeled data, which is expensive to acquire or often unavailable. In this work, we propose a novel label noise-resistant mean teaching approach (LNMT) for weakly supervised fake news detection. LNMT leverages unlabeled news and feedback comments of users to enlarge the amount of training data and facilitates model training by generating refined labels as weak supervision. Specifically, LNMT automatically assigns initial weak labels to unlabeled samples based on semantic correlation and emotional association between news content and the comments. Moreover, in order to suppress the noises in weak labels, LNMT establishes a mean teacher framework equipped with label propagation and label reliability estimation. The framework measures a weak label similarity matrix between the teacher and student networks, and propagates different valuable weak label information to refine the weak labels. Meanwhile, it exploits the consistency between the output class likelihood vectors of the two networks to evaluate the reliability of the weak labels and incorporates the reliability into model optimization to alleviate the negative effect of noisy weak labels. Extensive experiments show the superior performance of LNMT.

cs.CL↗

arXiv2022

Feasibility of Interactive 3D Map for Remote Sighted Assistance

Remote sighted assistance (RSA) has emerged as a conversational assistive technology, where remote sighted workers, i.e., agents, provide real-time assistance to users with vision impairments via video-chat-like communication. Researchers found that agents' lack of environmental knowledge, the difficulty of orienting users in their surroundings, and the inability to estimate distances from users' camera feeds are key challenges to sighted agents. To address these challenges, researchers have suggested assisting agents with computer vision technologies, especially 3D reconstruction. This paper presents a high-fidelity prototype of such an RSA, where agents use interactive 3D maps with localization capability. We conducted a walkthrough study with thirteen agents and one user with simulated vision impairment using this prototype. The study revealed that, compared to baseline RSA, the agents were significantly faster in providing navigational assistance to users, and their mental workload was significantly reduced -- all indicate the feasibility and prospect of 3D maps in RSA.

cs.HC↗

arXiv2020

SegFix: Model-Agnostic Boundary Refinement for Segmentation

We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model. Motivated by the empirical observation that the label predictions of interior pixels are more reliable, we propose to replace the originally unreliable predictions of boundary pixels by the predictions of interior pixels. Our approach processes only the input image through two steps: (i) localize the boundary pixels and (ii) identify the corresponding interior pixel for each boundary pixel. We build the correspondence by learning a direction away from the boundary pixel to an interior pixel. Our method requires no prior information of the segmentation models and achieves nearly real-time speed. We empirically verify that our SegFix consistently reduces the boundary errors for segmentation results generated from various state-of-the-art models on Cityscapes, ADE20K and GTA5. Code is available at: https://github.com/openseg-group/openseg.pytorch.

cs.CV↗

arXiv2019

ABD-Net: Attentive but Diverse Person Re-Identification

Attention mechanism has been shown to be effective for person re-identification (Re-ID). However, the learned attentive feature embeddings which are often not naturally diverse nor uncorrelated, will compromise the retrieval performance based on the Euclidean distance. We advocate that enforcing diversity could greatly complement the power of attention. To this end, we propose an Attentive but Diverse Network (ABD-Net), which seamlessly integrates attention modules and diversity regularization throughout the entire network, to learn features that are representative, robust, and more discriminative. Specifically, we introduce a pair of complementary attention modules, focusing on channel aggregation and position awareness, respectively. Furthermore, a new efficient form of orthogonality constraint is derived to enforce orthogonality on both hidden activations and weights. Through careful ablation studies, we verify that the proposed attentive and diverse terms each contributes to the performance gains of ABD-Net. On three popular benchmarks, ABD-Net consistently outperforms existing state-of-the-art methods.

cs.CV↗

arXiv2004

Multiple Round Quantum Dense Coding And Its Implementation Using Nuclear Magnetic Resonance

A multiple round quantum dense coding (MRQDC) scheme based on the quantum phase estimation algorithm is proposed. Using an $m+1$ qubit system, Bob can transmit $2^{m+1}$ messages to Alice, through manipulating only one qubit and exchanging it between Alice and Bob for $m$ rounds. The information capacity is enhanced to $m+1$ bits. We have implemented the scheme in a three- qubit nuclear magnetic resonance (NMR) quantum computer. The experimental results show a good agreement between theory and experiment.

quant-ph↗