arXiv subjects

Wei Ji

Publications and source records attributed to Wei Ji.

At least 199 records · Page 11Linked to original sources

arXiv2022

FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms

Short video platforms have become an important channel for news sharing, but also a new breeding ground for fake news. To mitigate this problem, research of fake news video detection has recently received a lot of attention. Existing works face two roadblocks: the scarcity of comprehensive and largescale datasets and insufficient utilization of multimodal information. Therefore, in this paper, we construct the largest Chinese short video dataset about fake news named FakeSV, which includes news content, user comments, and publisher profiles simultaneously. To understand the characteristics of fake news videos, we conduct exploratory analysis of FakeSV from different perspectives. Moreover, we provide a new multimodal detection model named SV-FEND, which exploits the cross-modal correlations to select the most informative features and utilizes the social context information for detection. Extensive experiments evaluate the superiority of the proposed method and provide detailed comparisons of different methods and modalities for future works.

cs.MM↗

arXiv2022

PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models

Vision-language pre-training (VLP) has shown impressive performance on a wide range of cross-modal tasks, where VLP models without reliance on object detectors are becoming the mainstream due to their superior computation efficiency and competitive performance. However, the removal of object detectors also deprives the capability of VLP models in explicit object modeling, which is essential to various position-sensitive vision-language (VL) tasks, such as referring expression comprehension and visual commonsense reasoning. To address the challenge, we introduce PEVL that enhances the pre-training and prompt tuning of VLP models with explicit object position modeling. Specifically, PEVL reformulates discretized object positions and language in a unified language modeling framework, which facilitates explicit VL alignment during pre-training, and also enables flexible prompt tuning for various downstream tasks. We show that PEVL enables state-of-the-art performance of detector-free VLP models on position-sensitive tasks such as referring expression comprehension and phrase grounding, and also improves the performance on position-insensitive tasks with grounded inputs. We make the data and code for this paper publicly available at https://github.com/thunlp/PEVL.

cs.CV↗

arXiv2022

Constraints on Spin-Spin-Velocity-Dependent Interaction

The existence of exotic spin-dependent forces may shine light on new physics beyond the Standard Model. We utilize two iron shielded SmCo$_5$ electron-spin sources and two optically pumped magnetometers to search for exotic long-range spin-spin-velocity-dependent force. The orientations of spin sources and magnetometers are optimized such that the exotic force is enhanced and common-mode noise is effectively subtracted. We set direct limit on proton-electron interaction in the force range from 1\,cm to 1\,km. Our experiment represents more than ten orders of magnitude improvement than previous works.

physics.atom-ph↗

arXiv2022

Boundary Proposal Network for Two-Stage Natural Language Video Localization

We aim to address the problem of Natural Language Video Localization (NLVL)-localizing the video segment corresponding to a natural language description in a long and untrimmed video. State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based approach: it first pre-defines a series of video segment candidates (e.g., by sliding window), and then does classification for each candidate; 2) anchor-free approach: it directly predicts the probabilities for each video frame as a boundary or intermediate frame inside the positive segment. However, both kinds of one-stage approaches have inherent drawbacks: the anchor-based approach is susceptible to the heuristic rules, further limiting the capability of handling videos with variant length. While the anchor-free approach fails to exploit the segment-level interaction thus achieving inferior results. In this paper, we propose a novel Boundary Proposal Network (BPNet), a universal two-stage framework that gets rid of the issues mentioned above. Specifically, in the first stage, BPNet utilizes an anchor-free model to generate a group of high-quality candidate video segments with their boundaries. In the second stage, a visual-language fusion layer is proposed to jointly model the multi-modal interaction between the candidate and the language query, followed by a matching score rating layer that outputs the alignment score for each candidate. We evaluate our BPNet on three challenging NLVL benchmarks (i.e., Charades-STA, TACoS and ActivityNet-Captions). Extensive experiments and ablative studies on these datasets demonstrate that the BPNet outperforms the state-of-the-art methods.

cs.CV↗

arXiv2022

Video Question Answering: Datasets, Algorithms and Challenges

Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos. It has earned increasing attention with recent research trends in joint vision and language understanding. Yet, compared with ImageQA, VideoQA is largely underexplored and progresses slowly. Although different algorithms have continually been proposed and shown success on different VideoQA datasets, we find that there lacks a meaningful survey to categorize them, which seriously impedes its advancements. This paper thus provides a clear taxonomy and comprehensive analyses to VideoQA, focusing on the datasets, algorithms, and unique challenges. We then point out the research trend of studying beyond factoid QA to inference QA towards the cognition of video contents, Finally, we conclude some promising directions for future exploration.

cs.CV↗

arXiv2022

Ultrasensitive atomic comagnetometer with enhanced nuclear spin coherence

Achieving high energy resolution in spin systems is important for fundamental physics research and precision measurements, with alkali-noble-gas comagnetometers being among the best available sensors. We found a new relaxation mechanism in such devices, the gradient of the Fermi-contact-interaction field that dominates the relaxation of hyperpolarized nuclear spins. We report on precise control over spin distribution, demonstrating a tenfold increase of nuclear spin hyperpolarization and transverse coherence time with optimal hybrid optical pumping. Operating in the self-compensation regime, our $^{21}$Ne-Rb-K comagnetometer achieves an ultrahigh inertial rotation sensitivity of $3\times10^{-8}$\,rad/s/Hz$^{1/2}$ in the frequency range from 0.2 to 1.0 Hz, which is equivalent to the energy resolution of $3.1\times 10^{-23}$\,eV/Hz$^{1/2}$. We propose to use this comagnetometer to search for exotic spin-dependent interactions involving proton and neutron spins. The projected sensitivity surpasses the previous experimental and astrophysical limits by more than four orders of magnitude.

physics.atom-ph↗

arXiv2022

Interweaving Polar Charge Orders in a Layered Metallic Super-atomic Crystal

Electronic properties of super-atomic crystals have not been sufficiently explored due to the versatility of their building units; moreover, their inter-unit couplings are even poorly understood. Here, we present a joint experiment-theory investigation of a rational-designed layered super-atomic crystal of Au6Te12Se8 cubes, stacked by non-covalent inter-cube quasi-bonds. We found a sequential-emerged anisotropic triple-cube charge-density-wave (tc-CDW) and polarized metallic states below 120 K, as revealed via scanning tunneling microscopy/spectroscopy, angle-resolved photoemission spectroscopy, transport measurement, Raman spectra, and density functional theory. The polarized states are locked in an anti-parallel configuration, which is required for maintaining the inversion symmetry of the center-cube in the tc-CDW. The anti-polar metallic states are thus interweaved by the charge-density-wave and the polarized metallic states, and primarily ascribed to electronic effects via theoretical calculations. This work not only demonstrates a microscopic picture of the interweaved CDW and polarized charge orders in the super-atomic crystal of ATS, but also sheds light on expanding the existing category of quantum materials to non-covalent solids.

cond-mat.mtrl-sci↗

arXiv2022

Fano Interference in a Single-Molecule Junction

Trends of miniaturized devices and quantum interference electronics lead to the long desire of Fano interference in single-molecule junctions, here, which is successfully demonstrated using the 2,7-di(4-pyridyl)-9,9'-spirobifluorene molecule with a long backbone group and a short side group. Experimentally, the two electrically coupled groups are found to contribute to two blurred degenerate points in the differential conductance mapping. This forms a characteristic non-centrosymmetric double-crossing feature, with distinct temperature response for each crossing. Theoretically, we describe the practical in-junction electron transmission using a new two-tunnelling-channel coupling model and obtain a working formula with a Fano term and a Breit-Wigner term. The formula is shown to provide a good fit for all the mapping data and their temperature dependence in three dimensions, identifying the Fano component. Our work thus forms a complete set of evidence of the Fano interference in a single-molecule junction induced by two-tunnelling-channel coupling transport. Density functional theory calculations are used to corroborate this new physics.

cond-mat.mes-hall↗

arXiv2022

MetaComp: Learning to Adapt for Online Depth Completion

Relying on deep supervised or self-supervised learning, previous methods for depth completion from paired single image and sparse depth data have achieved impressive performance in recent years. However, facing a new environment where the test data occurs online and differs from the training data in the RGB image content and depth sparsity, the trained model might suffer severe performance drop. To encourage the trained model to work well in such conditions, we expect it to be capable of adapting to the new environment continuously and effectively. To achieve this, we propose MetaComp. It utilizes the meta-learning technique to simulate adaptation policies during the training phase, and then adapts the model to new environments in a self-supervised manner in testing. Considering that the input is multi-modal data, it would be challenging to adapt a model to variations in two modalities simultaneously, due to significant differences in structure and form of the two modal data. Therefore, we further propose to disentangle the adaptation procedure in the basic meta-learning training into two steps, the first one focusing on the depth sparsity while the second attending to the image content. During testing, we take the same strategy to adapt the model online to new multi-modal data. Experimental results and comprehensive ablations show that our MetaComp is capable of adapting to the depth completion in a new environment effectively and robust to changes in different modalities.

cs.CV↗

arXiv2022

Fine-Grained Scene Graph Generation with Data Transfer

Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images. Recent works have made a steady progress on SGG, and provide useful tools for high-level vision and language understanding. However, due to the data distribution problems including long-tail distribution and semantic ambiguity, the predictions of current SGG models tend to collapse to several frequent but uninformative predicates (e.g., on, at), which limits practical application of these models in downstream tasks. To deal with the problems above, we propose a novel Internal and External Data Transfer (IETrans) method, which can be applied in a plug-and-play fashion and expanded to large SGG with 1,807 predicate classes. Our IETrans tries to relieve the data distribution problem by automatically creating an enhanced dataset that provides more sufficient and coherent annotations for all predicates. By training on the enhanced dataset, a Neural Motif model doubles the macro performance while maintaining competitive micro performance. The code and data are publicly available at https://github.com/waxnkw/IETrans-SGG.pytorch.

cs.CV↗

arXiv2022

Multi-state data storage in a two-dimensional stripy antiferromagnet implemented by magnetoelectric effect

A promising approach to the next generation of low-power, functional, and energy-efficient electronics relies on novel materials with coupled magnetic and electric degrees of freedom. In particular, stripy antiferromagnets often exhibit broken crystal and magnetic symmetries, which may bring about the magnetoelectric (ME) effect and enable the manipulation of intriguing properties and functionalities by electrical means. The demand for expanding the boundaries of data storage and processing technologies has led to the development of spintronics toward two-dimensional (2D) platforms. This work reports the ME effect in the 2D stripy antiferromagnetic insulator CrOCl down to a single layer. By measuring the tunneling resistance of CrOCl on the parameter space of temperature, magnetic field, and applied voltage, we verified the ME coupling down to the 2D limit and unraveled its mechanism. Utilizing the multi-stable states and ME coupling at magnetic phase transitions, we realize multi-state data storage in the tunneling devices. Our work not only advances the fundamental understanding of spin-charge coupling but also demonstrates the great potential of 2D antiferromagnetic materials to deliver devices and circuits beyond the traditional binary operations.

cond-mat.mtrl-sci↗

arXiv2022

One-step exfoliation method for plasmonic activation of large-area 2D crystals

Advanced exfoliation techniques are crucial for exploring the intrinsic properties and applications of 2D materials. Though the recently discovered Au-enhanced exfoliation technique provides an effective strategy for preparation of large-scale 2D crystals, the high cost of gold hinders this method from being widely adopted in industrial applications. In addition, direct Au contact could significantly quench photoluminescence (PL) emission in 2D semiconductors. It is therefore crucial to find alternative metals that can replace gold to achieve efficient exfoliation of 2D materials. Here, we present a one-step Ag-assisted method that can efficiently exfoliate many large-area 2D monolayers, where the yield ratio is comparable to Au-enhanced exfoliation method. Differing from Au film, however, the surface roughness of as-prepared Ag films on SiO2/Si substrate is much higher, which facilitates the generation of surface plasmons resulting from the nanostructures formed on the rough Ag surface. More interestingly, the strong coupling between 2D semiconductor crystals (e.g. MoS2, MoSe2) and Ag film leads to a unique PL enhancement that has not been observed in other mechanical exfoliation techniques, which can be mainly attributed to enhanced light-matter interaction as a result of extended propagation of surface plasmonic polariton (SPP). Our work provides a lower-cost and universal Ag-assisted exfoliation method, while at the same offering enhanced SPP-matter interactions.

cond-mat.mtrl-sci↗

arXiv2022

Realization of one-dimensional electronic flat bands in an untwisted moire superlattice

Two-dimensional electronic flat bands and their induced correlated electronic interactions have been discovered, probed, and tuned in interlayer regions of hexagonally shaped van der Waals moire superlattices. Fabrication of anisotropic one-dimensional correlated bands by moire interference of 2D, however, remains a challenge. Here, we report an experimental discovery of 1D electronic flat bands near the Fermi level in an anisotropic rectangular moire superlattice composed of in situ grown, vdW stacked two-atomic-layer thick Bi(110) well-aligned on a SnSe(001) substrate. The epitaxial lattice mismatch between the aligned Bi and SnSe zigzag atomic chains causes strong three-dimensional anisotropic atomic relaxations with associated one-dimensional out-of- and in-plane strain distributions that are expressed in electronic bands of the Bi(110) layer, which are characterized jointly by scanning probe microscopy and density functional theory. At the regions of the strongest out-of-plane shear strain, a series of 1D flat bands near the Fermi level are experimentally observed and defined in our calculations. We establish that 1D flat bands can arise in moiré superlattices in absence of the relative layer twist, but solely through the lattice strain. We generalize the strategy of utilizing strain in lattice mismatched rectangular hetero-bilayers for engineering correlated anisotropic electronic bands.

cond-mat.mes-hall↗

arXiv2022

Invariant Grounding for Video Question Answering

Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer. In leading VideoQA models, the typical learning objective, empirical risk minimization (ERM), latches on superficial correlations between video-question pairs and answers as the alignments. However, ERM can be problematic, because it tends to over-exploit the spurious correlations between question-irrelevant scenes and answers, instead of inspecting the causal effect of question-critical scenes. As a result, the VideoQA models suffer from unreliable reasoning. In this work, we first take a causal look at VideoQA and argue that invariant grounding is the key to ruling out the spurious correlations. Towards this end, we propose a new learning framework, Invariant Grounding for VideoQA (IGV), to ground the question-critical scene, whose causal relations with answers are invariant across different interventions on the complement. With IGV, the VideoQA models are forced to shield the answering process from the negative influence of spurious correlations, which significantly improves the reasoning ability. Experiments on three benchmark datasets validate the superiority of IGV in terms of accuracy, visual explainability, and generalization ability over the leading baselines.

cs.CV↗

arXiv2022

Layer-dependent interlayer antiferromagnetic spin reorientation in air-stable semiconductor CrSBr

Magnetic van der Waals (vdW) materials offer a fantastic platform to investigate and exploit rich spin configurations stabilized in reduced dimensions. One tantalizing magnetic order is the interlayer antiferromagnetism in A-type vdW antiferromagnet, which may be effectively modified by the magnetic field, stacking order and thickness scaling. However, atomically revealing the interlayer spin orientation in the vdW antiferromagnet is highly challenging, because most of the material candidates exhibit an insulating ground state or instability in ambient conditions. Here, we report the layer-dependent interlayer antiferromagnetic reorientation in air-stable semiconductor CrSBr using magnetotransport characterization and first-principles calculations. We reveal a pronounced odd-even layer effect of interlayer reorientation, which originates from the competitions among interlayer exchange, magnetic anisotropy energy and extra Zeeman energy of uncompensated magnetization. Furthermore, we quantitatively constructed the layer-dependent magnetic phase diagram with the help of a linear-chain model. Our work uncovers the layer-dependent interlayer antiferromagnetic reorientation engineered by magnetic field in the air-stable semiconductor, which could contribute to future vdW spintronic devices.

cond-mat.mtrl-sci↗

arXiv2022

Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection

Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques. Unfortunately, existing RGB-D SOD methods typically demand large quantity of training images being thoroughly annotated at pixel-level. The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios. On the other hand, current unsupervised RGB-D SOD methods still heavily rely on handcrafted feature representations. This inspires us to propose in this paper a deep unsupervised RGB-D saliency detection approach, which requires no manual pixel-level annotation during training. It is realized by two key ingredients in our training pipeline. First, a depth-disentangled saliency update (DSU) framework is designed to automatically produce pseudo-labels with iterative follow-up refinements, which provides more trustworthy supervision signals for training the saliency network. Second, an attentive training strategy is introduced to tackle the issue of noisy pseudo-labels, by properly re-weighting to highlight the more reliable pseudo-labels. Extensive experiments demonstrate the superior efficiency and effectiveness of our approach in tackling the challenging unsupervised RGB-D SOD scenarios. Moreover, our approach can also be adapted to work in fully-supervised situation. Empirical studies show the incorporation of our approach gives rise to notably performance improvement in existing supervised RGB-D SOD models.

cs.CV↗

arXiv2022

SAPPHIRE: Search for exotic parity-violation interactions with quantum spin amplifiers

Quantum sensing provides sensitive tabletop tools to search for exotic spin-dependent interactions beyond the Standard Model, which has attracted great attention in theories and experiments. Here we develop a technique based on quantum Spin Amplifier for Particle PHysIcs REsearch (SAPPHIRE) to resonantly search for exotic interactions, specifically parity-odd spin-spin interactions. The present technique effectively amplifies the pseudomagnetic field generated by exotic interactions by a factor of about 200 while being insensitive to spurious external magnetic fields. Our studies, using such a quantum amplification technique, open the doors to exploring the parity-violation interactions mediated by Z' bosons in the challenging parameter space (force range between 3 mm and 0.1 km) and set the most stringent constraints on Z'-mediated electron-neutron couplings, significantly improving previous limits by up to five orders of magnitude. Moreover, our bounds on Z'-mediated couplings between nucleons reaches into a hitherto unexplored parameter space (force range below 1 m), complementing the existing astrophysical and laboratory studies.

quant-ph↗

arXiv2022

Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations

Although single-image super-resolution (SISR) methods have achieved great success on single degradation, they still suffer performance drop with multiple degrading effects in real scenarios. Recently, some blind and non-blind models for multiple degradations have been explored. However, those methods usually degrade significantly for distribution shifts between the training and test data. Towards this end, we propose a conditional meta-network framework (named CMDSR) for the first time, which helps SR framework learn how to adapt to changes in input distribution. We extract degradation prior at task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet). Specifically, the ConditionNet of our framework first learns the degradation prior from a support set, which is composed of a series of degraded image patches from the same task. Then the adaptive BaseNet rapidly shifts its parameters according to the conditional features. Moreover, in order to better extract degradation prior, we propose a task contrastive loss to decrease the inner-task distance and increase the cross-task distance between task-level features. Without predefining degradation maps, our blind framework can conduct one single parameter update to yield considerable SR results. Extensive experiments demonstrate the effectiveness of CMDSR over various blind, even non-blind methods. The flexible BaseNet structure also reveals that CMDSR can be a general framework for large series of SISR models. Our code is available at \url{https://github.com/guanghaoyin/CMDSR}.

cs.CV↗