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Existing scene text removal (STR) task suffers from insufficient training data due to the expensive pixel-level labeling. In this paper, we aim to address this issue by introducing a Text-aware Masked Image Modeling algorithm (TMIM), which can pretrain STR models with low-cost text detection labels (e.g., text bounding box). Different from previous pretraining methods that use indirect auxiliary tasks only to enhance the implicit feature extraction ability, our TMIM first enables the STR task to be directly trained in a weakly supervised manner, which explores the STR knowledge explicitly and efficiently. In TMIM, first, a Background Modeling stream is built to learn background generation rules by recovering the masked non-text region. Meanwhile, it provides pseudo STR labels on the masked text region. Second, a Text Erasing stream is proposed to learn from the pseudo labels and equip the model with end-to-end STR ability. Benefiting from the two collaborative streams, our STR model can achieve impressive performance only with the public text detection datasets, which greatly alleviates the limitation of the high-cost STR labels. Experiments demonstrate that our method outperforms other pretrain methods and achieves state-of-the-art performance (37.35 PSNR on SCUT-EnsText). Code will be available at https://github.com/wzx99/TMIM., Comment: Accepted by ECCV 2024

Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few methods using graph kernels or graph neural networks (GNNs). Graph kernels often suffer from computational costs and manual feature engineering, while GNNs commonly utilize global pooling operations, risking the loss of structural or semantic information. This work introduces Graph Reference Distribution Learning (GRDL), an efficient and accurate graph classification method. GRDL treats each graph's latent node embeddings given by GNN layers as a discrete distribution, enabling direct classification without global pooling, based on maximum mean discrepancy to adaptively learned reference distributions. To fully understand this new model (the existing theories do not apply) and guide its configuration (e.g., network architecture, references' sizes, number, and regularization) for practical use, we derive generalization error bounds for GRDL and verify them numerically. More importantly, our theoretical and numerical results both show that GRDL has a stronger generalization ability than GNNs with global pooling operations. Experiments on moderate-scale and large-scale graph datasets show the superiority of GRDL over the state-of-the-art, emphasizing its remarkable efficiency, being at least 10 times faster than leading competitors in both training and inference stages.

Recently, scene text recognition (STR) models have shown significant performance improvements. However, existing models still encounter difficulties in recognizing challenging texts that involve factors such as severely distorted and perspective characters. These challenging texts mainly cause two problems: (1) Large Intra-Class Variance. (2) Small Inter-Class Variance. An extremely distorted character may prominently differ visually from other characters within the same category, while the variance between characters from different classes is relatively small. To address the above issues, we propose a novel method that enriches the character features to enhance the discriminability of characters. Firstly, we propose the Character-Aware Constraint Encoder (CACE) with multiple blocks stacked. CACE introduces a decay matrix in each block to explicitly guide the attention region for each token. By continuously employing the decay matrix, CACE enables tokens to perceive morphological information at the character level. Secondly, an Intra-Inter Consistency Loss (I^2CL) is introduced to consider intra-class compactness and inter-class separability at feature space. I^2CL improves the discriminative capability of features by learning a long-term memory unit for each character category. Trained with synthetic data, our model achieves state-of-the-art performance on common benchmarks (94.1% accuracy) and Union14M-Benchmark (61.6% accuracy). Code is available at https://github.com/bang123-box/CFE., Comment: Accepted to IJCAI2024

Few-shot gradient methods have been extensively utilized in existing model pruning methods, where the model weights are regarded as static values and the effects of potential weight perturbations are not considered. However, the widely used large language models (LLMs) have several billion model parameters, which could increase the fragility of few-shot gradient pruning. In this work, we experimentally show that one-shot gradient pruning algorithms could lead to unstable results under perturbations to model weights. And the minor error of switching between data formats bfloat16 and float16 could result in drastically different outcomes. To address such instabilities, we leverage optimization analysis and propose an LLM structural pruning method, called MoreauPruner, with provable robustness against weight perturbations. In MoreauPruner, the model weight importance is estimated based on the neural network's Moreau envelope, which can be flexibly combined with $\ell_1$-norm regularization techniques to induce the sparsity required in the pruning task. We extensively evaluate the MoreauPruner algorithm on several well-known LLMs, including LLaMA-7B, LLaMA-13B, LLaMA3-8B, and Vicuna-7B. Our numerical results suggest the robustness of MoreauPruner against weight perturbations, and indicate the MoreauPruner's successful accuracy-based scores in comparison to several existing pruning methods. We have released the code in \url{https://github.com/ShiningSord/MoreauPruner}.

In text recognition, self-supervised pre-training emerges as a good solution to reduce dependence on expansive annotated real data. Previous studies primarily focus on local visual representation by leveraging mask image modeling or sequence contrastive learning. However, they omit modeling the linguistic information in text images, which is crucial for recognizing text. To simultaneously capture local character features and linguistic information in visual space, we propose Symmetric Superimposition Modeling (SSM). The objective of SSM is to reconstruct the direction-specific pixel and feature signals from the symmetrically superimposed input. Specifically, we add the original image with its inverted views to create the symmetrically superimposed inputs. At the pixel level, we reconstruct the original and inverted images to capture character shapes and texture-level linguistic context. At the feature level, we reconstruct the feature of the same original image and inverted image with different augmentations to model the semantic-level linguistic context and the local character discrimination. In our design, we disrupt the character shape and linguistic rules. Consequently, the dual-level reconstruction facilitates understanding character shapes and linguistic information from the perspective of visual texture and feature semantics. Experiments on various text recognition benchmarks demonstrate the effectiveness and generality of SSM, with 4.1% average performance gains and 86.6% new state-of-the-art average word accuracy on Union14M benchmarks. The code is available at https://github.com/FaltingsA/SSM., Comment: Accepted to IJCAI2024

Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention. In this paper, we propose ChatPattern, a novel Large-Language-Model (LLM) powered framework for flexible pattern customization. ChatPattern utilizes a two-part system featuring an expert LLM agent and a highly controllable layout pattern generator. The LLM agent can interpret natural language requirements and operate design tools to meet specified needs, while the generator excels in conditional layout generation, pattern modification, and memory-friendly patterns extension. Experiments on challenging pattern generation setting shows the ability of ChatPattern to synthesize high-quality large-scale patterns., Comment: Accepted by DAC24

Emerging industrial applications involving robotic collaborative operations and mobile robots require a more reliable and precise wireless network for deterministic data transmission. To meet this demand, the 3rd Generation Partnership Project (3GPP) is promoting the integration of 5th Generation Mobile Communication Technology (5G) and Time-Sensitive Networking (TSN). Time synchronization is essential for deterministic data transmission. Based on the 3GPP's vision of the 5G and TSN integrated networking with interoperability, we improve the time synchronization of TSN to conquer the multi-gNB competition, re-transmission, and mobility problems for the integrated 5G time synchronization. We implemented the improvement mechanisms and systematically validated the performance of 5G+TSN time synchronization. Based on the simulation in 500m x 500m industrial environments, the improved time synchronization achieved a precision of 1 microsecond with interoperability between 5G nodes and TSN nodes.

The discrete distribution is often used to describe complex instances in machine learning, such as images, sequences, and documents. Traditionally, clustering of discrete distributions (D2C) has been approached using Wasserstein barycenter methods. These methods operate under the assumption that clusters can be well-represented by barycenters, which is seldom true in many real-world applications. Additionally, these methods are not scalable for large datasets due to the high computational cost of calculating Wasserstein barycenters. In this work, we explore the feasibility of using spectral clustering combined with distribution affinity measures (e.g., maximum mean discrepancy and Wasserstein distance) to cluster discrete distributions. We demonstrate that these methods can be more accurate and efficient than barycenter methods. To further enhance scalability, we propose using linear optimal transport to construct affinity matrices efficiently for large datasets. We provide theoretical guarantees for the success of our methods in clustering distributions. Experiments on both synthetic and real data show that our methods outperform existing baselines.

Modern scientific applications predominantly run on large-scale computing platforms, necessitating collaboration between scientific domain experts and high-performance computing (HPC) experts. While domain experts are often skilled in customizing domain-specific scientific computing routines, which often involves various matrix computations, HPC experts are essential for achieving efficient execution of these computations on large-scale platforms. This process often involves utilizing complex parallel computing libraries tailored to specific matrix computation scenarios. However, the intricate programming procedure and the need for deep understanding in both application domains and HPC poses significant challenges to the widespread adoption of scientific computing. In this research, we observe that matrix computations can be transformed into equivalent graph representations, and that by utilizing graph processing engines, HPC experts can be freed from the burden of implementing efficient scientific computations. Based on this observation, we introduce a graph engine-based scientific computing (Graph for Science) paradigm, which provides a unified graph programming interface, enabling domain experts to promptly implement various types of matrix computations. The proposed paradigm leverages the underlying graph processing engine to achieve efficient execution, eliminating the needs for HPC expertise in programming large-scale scientific applications. Our results show that the graph engine-based scientific computing paradigm achieves performance comparable to the best-performing implementations based on existing parallel computing libraries and bespoke implementations. Importantly, the paradigm greatly simplifies the development of scientific computations on large-scale platforms, reducing the programming difficulty for scientists and facilitating broader adoption of scientific computing.

We consider the task of identifying and estimating a parameter of interest in settings where data is missing not at random (MNAR). In general, such parameters are not identified without strong assumptions on the missing data model. In this paper, we take an alternative approach and introduce a method inspired by data fusion, where information in an MNAR dataset is augmented by information in an auxiliary dataset subject to missingness at random (MAR). We show that even if the parameter of interest cannot be identified given either dataset alone, it can be identified given pooled data, under two complementary sets of assumptions. We derive an inverse probability weighted (IPW) estimator for identified parameters, and evaluate the performance of our estimation strategies via simulation studies, and a data application., Comment: 22 pages, 3 figures

The evaluation of deep generative models has been extensively studied in the centralized setting, where the reference data are drawn from a single probability distribution. On the other hand, several applications of generative models concern distributed settings, e.g. the federated learning setting, where the reference data for conducting evaluation are provided by several clients in a network. In this paper, we study the evaluation of generative models in such distributed contexts with potentially heterogeneous data distributions across clients. We focus on the widely-used distance-based evaluation metrics, Fr\'echet Inception Distance (FID) and Kernel Inception Distance (KID). In the case of KID metric, we prove that scoring a group of generative models using the clients' averaged KID score will result in the same ranking as that of a centralized KID evaluation over a collective reference set containing all the clients' data. In contrast, we show the same result does not apply to the FID-based evaluation. We provide examples in which two generative models are assigned the same FID score by each client in a distributed setting, while the centralized FID scores of the two models are significantly different. We perform several numerical experiments on standard image datasets and generative models to support our theoretical results on the distributed evaluation of generative models using FID and KID scores., Comment: 26 pages, 22 figures

In this paper, we explore the potential of the Contrastive Language-Image Pretraining (CLIP) model in scene text recognition (STR), and establish a novel Symmetrical Linguistic Feature Distillation framework (named CLIP-OCR) to leverage both visual and linguistic knowledge in CLIP. Different from previous CLIP-based methods mainly considering feature generalization on visual encoding, we propose a symmetrical distillation strategy (SDS) that further captures the linguistic knowledge in the CLIP text encoder. By cascading the CLIP image encoder with the reversed CLIP text encoder, a symmetrical structure is built with an image-to-text feature flow that covers not only visual but also linguistic information for distillation.Benefiting from the natural alignment in CLIP, such guidance flow provides a progressive optimization objective from vision to language, which can supervise the STR feature forwarding process layer-by-layer.Besides, a new Linguistic Consistency Loss (LCL) is proposed to enhance the linguistic capability by considering second-order statistics during the optimization. Overall, CLIP-OCR is the first to design a smooth transition between image and text for the STR task.Extensive experiments demonstrate the effectiveness of CLIP-OCR with 93.8% average accuracy on six popular STR benchmarks.Code will be available at https://github.com/wzx99/CLIPOCR., Comment: Accepted by ACM MM 2023

Deep generative models dominate the existing literature in layout pattern generation. However, leaving the guarantee of legality to an inexplicable neural network could be problematic in several applications. In this paper, we propose \tool{DiffPattern} to generate reliable layout patterns. \tool{DiffPattern} introduces a novel diverse topology generation method via a discrete diffusion model with compute-efficiently lossless layout pattern representation. Then a white-box pattern assessment is utilized to generate legal patterns given desired design rules. Our experiments on several benchmark settings show that \tool{DiffPattern} significantly outperforms existing baselines and is capable of synthesizing reliable layout patterns., Comment: DAC2023 Accepted

Learning with noisy label (LNL) is a classic problem that has been extensively studied for image tasks, but much less for video in the literature. A straightforward migration from images to videos without considering the properties of videos, such as computational cost and redundant information, is not a sound choice. In this paper, we propose two new strategies for video analysis with noisy labels: 1) A lightweight channel selection method dubbed as Channel Truncation for feature-based label noise detection. This method selects the most discriminative channels to split clean and noisy instances in each category; 2) A novel contrastive strategy dubbed as Noise Contrastive Learning, which constructs the relationship between clean and noisy instances to regularize model training. Experiments on three well-known benchmark datasets for video classification show that our proposed tru{\bf N}cat{\bf E}-split-contr{\bf A}s{\bf T} (NEAT) significantly outperforms the existing baselines. By reducing the dimension to 10\% of it, our method achieves over 0.4 noise detection F1-score and 5\% classification accuracy improvement on Mini-Kinetics dataset under severe noise (symmetric-80\%). Thanks to Noise Contrastive Learning, the average classification accuracy improvement on Mini-Kinetics and Sth-Sth-V1 is over 1.6\%., Comment: Accepted by AAAI 2023

In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model. This model generalizes graph diffusion in different feature spaces, and uses the inception module to avoid the large amount of computations caused by complex network structures. We evaluate GIDN model on Open Graph Benchmark(OGB) datasets, reached an 11% higher performance than AGDN on ogbl-collab dataset.

The topological Weyl semimetals with peculiar band structure exhibit novel nonlinear optical enhancement phenomena even for light at optical wavelengths. While many intriguing nonlinear optical effects were constantly uncovered in type-I semimetals, few experimental works focused on basic nonlinear optical properties in type-II Weyl semimetals. Here we perform a fundamental static and time-resolved second harmonic generation (SHG) on the three dimensional Type-II Weyl semimetal candidate $\beta$-WP$_2$. Although $\beta$-WP$_2$ exhibits extremely high conductivity and an extraordinarily large mean free path, the second harmonic generation is unscreened by conduction electrons, we observed rather strong SHG response compared to non-topological polar metals and archetypal ferroelectric insulators. Additionally, our time-resolved SHG experiment traces ultrafast symmetry switching and reveals that polar metal $\beta$-WP$_2$ tends to form inversion symmetric metastable state after photo-excitation. Intense femtosecond laser pulse could optically drive symmetry switching and tune nonlinear optical response on ultrafast timescales although the interlayer coupling of $\beta$-WP$_2$ is very strong. Our work is illuminating for the polar metal nonlinear optics and potential ultrafast topological optoelectronic applications., Comment: 8 pages, 5 figures

Nonlinear responses of superconductors to intense terahertz radiation has been an active research frontier. Using terahertz pump-terahertz probe spectroscopy, we investigate the c-axis nonlinear optical response of a high-temperature superconducting cuprate. After excitation by a single-cycle terahertz pump pulse, the reflectivity of the probe pulse oscillates as the pump-probe delay is varied. Interestingly, the oscillatory central frequency scales linearly with the probe frequency, a fact widely overlooked in pump-probe experiments. By theoretically solving the nonlinear optical reflection problem on the interface, we show that our observation is well explained by the Josephson-type third-order nonlinear electrodynamics, together with the emission coefficient from inside the material into free space. The latter results in a strong enhancement of the emitted signal whose physical frequency is around the Josephson plasma edge. Our result offers a benchmark for and new insights into strong-field terahertz spectroscopy of related quantum materials., Comment: 8 pages, 4 figures