arXiv subjects

Liang Qiao

Publications and source records attributed to Liang Qiao.

At least 37 records · Page 2Linked to original sources

arXiv2023

H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation

Recently, deep learning methods have been widely used for tumor segmentation of multimodal medical images with promising results. However, most existing methods are limited by insufficient representational ability, specific modality number and high computational complexity. In this paper, we propose a hybrid densely connected network for tumor segmentation, named H-DenseFormer, which combines the representational power of the Convolutional Neural Network (CNN) and the Transformer structures. Specifically, H-DenseFormer integrates a Transformer-based Multi-path Parallel Embedding (MPE) module that can take an arbitrary number of modalities as input to extract the fusion features from different modalities. Then, the multimodal fusion features are delivered to different levels of the encoder to enhance multimodal learning representation. Besides, we design a lightweight Densely Connected Transformer (DCT) block to replace the standard Transformer block, thus significantly reducing computational complexity. We conduct extensive experiments on two public multimodal datasets, HECKTOR21 and PI-CAI22. The experimental results show that our proposed method outperforms the existing state-of-the-art methods while having lower computational complexity. The source code is available at https://github.com/shijun18/H-DenseFormer.

eess.IV↗

arXiv2023

Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers with Partially Annotated Ultrasound Images

Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automaticCAD system, lesion detection is critical for the following diagnosis. However, existing DL-based methods generally require voluminous manually-annotated region of interest (ROI) labels and class labels to train both the lesion detection and diagnosis models. In clinical practice, the ROI labels, i.e. ground truths, may not always be optimal for the classification task due to individual experience of sonologists, resulting in the issue of coarse annotation that limits the diagnosis performance of a CAD model. To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to enhance diagnostic accuracy of the ultrasound-based CAD for breast cancers. In particular, all the ROI-level labels are considered as coarse labels in the first training stage, and then a candidate selection mechanism is designed to identify optimallesion areas for both the fully and partially annotated samples. It refines the current ROI-level labels in the fully annotated images and the detected ROIs in the partially annotated samples with a weakly supervised manner under the guidance of class labels. In the second training stage, a self-distillation strategy further is further proposed to integrate the detection network and classification network into a unified framework as the final CAD model for joint optimization, which then further improves the diagnosis performance. The proposed TSDDNet is evaluated on a B-mode ultrasound dataset, and the experimental results show that it achieves the best performance on both lesion detection and diagnosis tasks, suggesting promising application potential.

eess.IV↗

arXiv2023

Language Adaptive Weight Generation for Multi-task Visual Grounding

Although the impressive performance in visual grounding, the prevailing approaches usually exploit the visual backbone in a passive way, i.e., the visual backbone extracts features with fixed weights without expression-related hints. The passive perception may lead to mismatches (e.g., redundant and missing), limiting further performance improvement. Ideally, the visual backbone should actively extract visual features since the expressions already provide the blueprint of desired visual features. The active perception can take expressions as priors to extract relevant visual features, which can effectively alleviate the mismatches. Inspired by this, we propose an active perception Visual Grounding framework based on Language Adaptive Weights, called VG-LAW. The visual backbone serves as an expression-specific feature extractor through dynamic weights generated for various expressions. Benefiting from the specific and relevant visual features extracted from the language-aware visual backbone, VG-LAW does not require additional modules for cross-modal interaction. Along with a neat multi-task head, VG-LAW can be competent in referring expression comprehension and segmentation jointly. Extensive experiments on four representative datasets, i.e., RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, validate the effectiveness of the proposed framework and demonstrate state-of-the-art performance.

cs.CV↗

arXiv2022

Rotational symmetry breaking in superconducting nickelate Nd0.8Sr0.2NiO2 films

The infinite-layer nickelates, isostructural to the high-Tc superconductor cuprates, have risen as a promising platform to host unconventional superconductivity and stimulated growing interests in the condensed matter community. Despite numerous researches, the superconducting pairing symmetry of the nickelate superconductors, the fundamental characteristic of a superconducting state, is still under debate. Moreover, the strong electronic correlation in the nickelates may give rise to a rich phase diagram, where the underlying interplay between the superconductivity and other emerging quantum states with broken symmetry is awaiting exploration. Here, we study the angular dependence of the transport properties on the infinite-layer nickelate Nd0.8Sr0.2NiO2 superconducting films with Corbino-disk configuration. The azimuthal angular dependence of the magnetoresistance (R(ϕ)) manifests the rotational symmetry breaking from isotropy to four-fold (C4) anisotropy with increasing magnetic field, revealing a symmetry breaking phase transition. Approaching the low temperature and large magnetic field regime, an additional two-fold (C2) symmetric component in the R(ϕ) curves and an anomalous upturn of the temperature-dependent critical field are observed simultaneously, suggesting the emergence of an exotic electronic phase. Our work uncovers the evolution of the quantum states with different rotational symmetries and provides deep insight into the global phase diagram of the nickelate superconductors.

cond-mat.supr-con↗

arXiv2022

Charge density waves in infinite-layer NdNiO$_2$ nickelates

In materials science, much effort has been devoted to reproduce superconductivity in chemical compositions analogous to cuprate superconductors since their discovery over thirty years ago. This approach was recently successful in realising superconductivity in infinite-layer nickelates. Although differing from cuprates in electronic and magnetic properties, strong Coulomb interactions suggest infinite-layer nickelates have a propensity to various symmetry-breaking orders that populate the cuprates. Here we report the observation of charge density waves (CDWs) in infinite-layer NdNiO$_2$ films using Ni-$L_3$ resonant x-ray scattering. Remarkably, CDWs form in Nd 5$d$ and Ni 3$d$ orbitals at the same commensurate wavevector $(0.333, 0)\;r.l.u.$, with non-negligible out-of-plane dependence, and an in-plane correlation length up to $\sim$ 60 Angstrom. Spectroscopic studies reveal a strong connection between CDWs and the Nd 5$d$ - Ni 3$d$ orbital hybridisation. Upon entering the superconducting state at 20\% Sr doping, the CDWs disappear. Our work demonstrates the existence of CDWs in infinite-layer nickelates with a multi-orbital character distinct from cuprates, which establishes their low-energy physics.

cond-mat.supr-con↗

arXiv2022

Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting

End-to-end text spotting has attached great attention recently due to its benefits on global optimization and high maintainability for real applications. However, the input scale has always been a tough trade-off since recognizing a small text instance usually requires enlarging the whole image, which brings high computational costs. In this paper, to address this problem, we propose a novel cost-efficient Dynamic Low-resolution Distillation (DLD) text spotting framework, which aims to infer images in different small but recognizable resolutions and achieve a better balance between accuracy and efficiency. Concretely, we adopt a resolution selector to dynamically decide the input resolutions for different images, which is constraint by both inference accuracy and computational cost. Another sequential knowledge distillation strategy is conducted on the text recognition branch, making the low-res input obtains comparable performance to a high-res image. The proposed method can be optimized end-to-end and adopted in any current text spotting framework to improve the practicability. Extensive experiments on several text spotting benchmarks show that the proposed method vastly improves the usability of low-res models. The code is available at https://github.com/hikopensource/DAVAR-Lab-OCR/.

cs.CV↗

arXiv2022

DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding

This paper presents DavarOCR, an open-source toolbox for OCR and document understanding tasks. DavarOCR currently implements 19 advanced algorithms, covering 9 different task forms. DavarOCR provides detailed usage instructions and the trained models for each algorithm. Compared with the previous opensource OCR toolbox, DavarOCR has relatively more complete support for the sub-tasks of the cutting-edge technology of document understanding. In order to promote the development and application of OCR technology in academia and industry, we pay more attention to the use of modules that different sub-domains of technology can share. DavarOCR is publicly released at https://github.com/hikopensource/Davar-Lab-OCR.

cs.CV↗

arXiv2022

MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective

NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective. The proposed approach contains two mutual information-based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rote memorizing entity names or exploiting biased cues in data. Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities.

cs.CL↗

arXiv2022

LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment

Table structure recognition is a challenging task due to the various structures and complicated cell spanning relations. Previous methods handled the problem starting from elements in different granularities (rows/columns, text regions), which somehow fell into the issues like lossy heuristic rules or neglect of empty cell division. Based on table structure characteristics, we find that obtaining the aligned bounding boxes of text region can effectively maintain the entire relevant range of different cells. However, the aligned bounding boxes are hard to be accurately predicted due to the visual ambiguities. In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features. Specifically, we propose the framework of Local and Global Pyramid Mask Alignment, which adopts the soft pyramid mask learning mechanism in both the local and global feature maps. It allows the predicted boundaries of bounding boxes to break through the limitation of original proposals. A pyramid mask re-scoring module is then integrated to compromise the local and global information and refine the predicted boundaries. Finally, we propose a robust table structure recovery pipeline to obtain the final structure, in which we also effectively solve the problems of empty cells locating and division. Experimental results show that the proposed method achieves competitive and even new state-of-the-art performance on several public benchmarks.

cs.CV↗

arXiv2022

Vitamin K content of cheese, yoghurt and meat products in Australia

Vitamin K is vital for normal blood coagulation, and may influence bone, neurological and vascular health. Data on the vitamin K content of Australian foods are limited, preventing estimation of vitamin K intakes in the Australian population. We measured phylloquinone (PK) and menaquinone (MK) -4 to -10 in cheese, yoghurt and meat products (48 composite samples from 288 primary samples) by liquid chromatography with electrospray ionisation-tandem mass spectrometry. At least one K vitamer was found in every sample. The greatest mean concentrations of PK, MK-4 and MK-9 were found in lamb liver, chicken leg meat and Cheddar cheese, respectively. Cheddar cheese and cream cheese contained MK-5. MK-8 was found in Cheddar cheese only. As the K vitamer profile and concentrations appear to vary considerably by geographical location, Australia needs a vitamin K food composition dataset that is representative of foods consumed in Australia.

q-bio.OT↗

arXiv2022

Unusual Hole-doping-dependent Electronic Instability and Electron-Phonon Coupling in Infinite-layer Nickelates

The interplay between superconductivity and charge density waves (CDWs) under hole doping in cuprates is one of the central phenomena in condensed matter physics. Recently, CDWs are also observed in CaCuO$_2$-analogous nickelates RNiO$_2$ (R = La, Nd) but exhibit fundamentally different hole-doping-dependent behaviors compared to that in cuprates, raising a challenging question on its origin. In this article, we propose that electronic instability (EI) and moment-dependent electron-phonon coupling (MEPC), mainly contributed by Ni 3dx2-y2 and R 5dz2, respectively, may be the possible reasons for CDW formation in RNiO$_2$. Without hole doping, a strong Fermi surface nesting (FSN) induced by the unique feature of van Hove singularity (VHS) across Fermi level exists in RNiO$_2$ but not in CaCuO$_2$, and the unusual temperature-insensitive feature of EI and MEPC could result in rather high temperature CDWs in RNiO$_2$. Under hole doping, the reduced FSN of Ni 3dx2-y2 by the shift of VHS and decreased occupation of R 5dz2 largely weaken EI and MEPC in RNiO$_2$, respectively, suppressing the CDW formation. Our theory not only offers possible explanations to some puzzling experimental observations, but also establishes a unified understanding on the hole-doping-dependent EI and MEPC in nickelates and cuprates.

cond-mat.supr-con↗

arXiv2022

Stability of Superconducting Nd0.8Sr0.2NiO2 Thin Films

The discovery of superconducting states in the nickelate thin film with infinite-layer structure has paved a new way for studying unconventional superconductivity. So far, research in this field is still very limited due to difficulties in sample preparation. Here we report on the successful preparation of superconducting Nd0.8Sr0.2NiO2 thin film (Tc = 8.0 - 11.1 K) and study the stability of such films in ambient environment, water and under electrochemical conditions. Our work demonstrates that the superconducting state of Nd0.8Sr0.2NiO2 is remarkably stable, which can last for at least 47 days continuous exposure to air at 20 degree Celsius and 35% relative humidity. Further we show the superconductivity disappears after being immersed in de-ionized water at room temperature for 5 hours. Surprisingly, it can also survive under ionic liquid gating conditions with applied voltage up to 4 V, which is even more stable than conventional perovskite complex oxides.

cond-mat.supr-con↗

arXiv2022

Photo-Induced Ultrafast Symmetry Switch in SnSe

Layered tin selenide (SnSe) has recently emerged as a high-performance thermoelectric material with the current record for the figure of merit (ZT) observed in the high-temperature Cmcm phase. So far, access of the Cmcm phase has been mainly obtained via thermal equilibrium methods based on sample heating or application of external pressure, thus restricting the current understanding only to ground-state conditions. Here, we investigate the ultrafast carrier and phononic dynamics in SnSe. Our results demonstrate that optical excitations can transiently switch the point-group symmetry of the crystal from Pnma to Cmcm at room temperature in a few hundreds of femtoseconds with an ultralow threshold for the excitation carrier density. This non-equilibrium Cmcm phase is found to be driven by the displacive excitation of coherent Ag phonons and, given the absence of low-energy thermal phonons, exists in SnSe with the status of 'cold lattice with hot carriers'. Our findings provide important insight for understanding non-equilibrium thermoelectric properties of SnSe.

cond-mat.mtrl-sci↗

arXiv2021

Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting

Many approaches have recently been proposed to detect irregular scene text and achieved promising results. However, their localization results may not well satisfy the following text recognition part mainly because of two reasons: 1) recognizing arbitrary shaped text is still a challenging task, and 2) prevalent non-trainable pipeline strategies between text detection and text recognition will lead to suboptimal performances. To handle this incompatibility problem, in this paper we propose an end-to-end trainable text spotting approach named Text Perceptron. Concretely, Text Perceptron first employs an efficient segmentation-based text detector that learns the latent text reading order and boundary information. Then a novel Shape Transform Module (abbr. STM) is designed to transform the detected feature regions into regular morphologies without extra parameters. It unites text detection and the following recognition part into a whole framework, and helps the whole network achieve global optimization. Experiments show that our method achieves competitive performance on two standard text benchmarks, i.e., ICDAR 2013 and ICDAR 2015, and also obviously outperforms existing methods on irregular text benchmarks SCUT-CTW1500 and Total-Text.

cs.CV↗

arXiv2021

TRIE: End-to-End Text Reading and Information Extraction for Document Understanding

Since real-world ubiquitous documents (e.g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic. Most existing works decouple the problem into two separate tasks, (1) text reading for detecting and recognizing texts in images and (2) information extraction for analyzing and extracting key elements from previously extracted plain text. However, they mainly focus on improving information extraction task, while neglecting the fact that text reading and information extraction are mutually correlated. In this paper, we propose a unified end-to-end text reading and information extraction network, where the two tasks can reinforce each other. Specifically, the multimodal visual and textual features of text reading are fused for information extraction and in turn, the semantics in information extraction contribute to the optimization of text reading. On three real-world datasets with diverse document images (from fixed layout to variable layout, from structured text to semi-structured text), our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.

cs.CV↗

arXiv2021

MANGO: A Mask Attention Guided One-Stage Scene Text Spotter

Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods attempt to develop various region of interest (RoI) operations to concatenate the detection part and the sequence recognition part into a two-stage text spotting framework. However, in such framework, the recognition part is highly sensitive to the detected results (e.g.), the compactness of text contours). To address this problem, in this paper, we propose a novel Mask AttentioN Guided One-stage text spotting framework named MANGO, in which character sequences can be directly recognized without RoI operation. Concretely, a position-aware mask attention module is developed to generate attention weights on each text instance and its characters. It allows different text instances in an image to be allocated on different feature map channels which are further grouped as a batch of instance features. Finally, a lightweight sequence decoder is applied to generate the character sequences. It is worth noting that MANGO inherently adapts to arbitrary-shaped text spotting and can be trained end-to-end with only coarse position information (e.g.), rectangular bounding box) and text annotations. Experimental results show that the proposed method achieves competitive and even new state-of-the-art performance on both regular and irregular text spotting benchmarks, i.e., ICDAR 2013, ICDAR 2015, Total-Text, and SCUT-CTW1500.

cs.CV↗

arXiv2021

VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations

Document layout analysis is crucial for understanding document structures. On this task, vision and semantics of documents, and relations between layout components contribute to the understanding process. Though many works have been proposed to exploit the above information, they show unsatisfactory results. NLP-based methods model layout analysis as a sequence labeling task and show insufficient capabilities in layout modeling. CV-based methods model layout analysis as a detection or segmentation task, but bear limitations of inefficient modality fusion and lack of relation modeling between layout components. To address the above limitations, we propose a unified framework VSR for document layout analysis, combining vision, semantics and relations. VSR supports both NLP-based and CV-based methods. Specifically, we first introduce vision through document image and semantics through text embedding maps. Then, modality-specific visual and semantic features are extracted using a two-stream network, which are adaptively fused to make full use of complementary information. Finally, given component candidates, a relation module based on graph neural network is incorported to model relations between components and output final results. On three popular benchmarks, VSR outperforms previous models by large margins. Code will be released soon.

cs.CV↗

arXiv2021

Hero: On the Chaos When PATH Meets Modules

Ever since its first release in 2009, the Go programming language (Golang) has been well received by software communities. A major reason for its success is the powerful support of library-based development, where a Golang project can be conveniently built on top of other projects by referencing them as libraries. As Golang evolves, it recommends the use of a new library-referencing mode to overcome the limitations of the original one. While these two library modes are incompatible, both are supported by the Golang ecosystem. The heterogeneous use of library-referencing modes across Golang projects has caused numerous dependency management (DM) issues, incurring reference inconsistencies and even build failures. Motivated by the problem, we conducted an empirical study to characterize the DM issues, understand their root causes, and examine their fixing solutions. Based on our findings, we developed \textsc{Hero}, an automated technique to detect DM issues and suggest proper fixing solutions. We applied \textsc{Hero} to 19,000 popular Golang projects. The results showed that \textsc{Hero} achieved a high detection rate of 98.5\% on a DM issue benchmark and found 2,422 new DM issues in 2,356 popular Golang projects. We reported 280 issues, among which 181 (64.6\%) issues have been confirmed, and 160 of them (88.4\%) have been fixed or are under fixing. Almost all the fixes have adopted our fixing suggestions.

cs.SE↗