Search Results for author:
Found 49 papers, 21 papers with code
ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer
To this end, we introduce a new model named Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter), which achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder.
RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation
This paper introduces RoMe, a novel framework designed for the robust reconstruction of large-scale road surfaces.
SPTS: Single-Point Text Spotting
For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotation of a single-point for each instance.
Ranked #3 on
Text Spotting
on SCUT-CTW1500
SPTS v2: Single-Point Scene Text Spotting
Within the context of our SPTS v2 framework, our experiments suggest a potential preference for single-point representation in scene text spotting when compared to other representations.
Ranked #15 on
Text Spotting
on ICDAR 2015
Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution
Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education.
A Vision-Centric Approach for Static Map Element Annotation
The recent development of online static map element (a. k. a.
M6Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis
Document layout analysis is a crucial prerequisite for document understanding, including document retrieval and conversion.
Towards Accurate Ground Plane Normal Estimation from Ego-Motion
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles.
Inverse design of two-dimensional materials with invertible neural networks
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties.
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
Federated Learning (FL) framework brings privacy benefits to distributed learning systems by allowing multiple clients to participate in a learning task under the coordination of a central server without exchanging their private data.
Can Programming Languages Boost Each Other via Instruction Tuning?
Results demonstrate that programming languages can significantly improve each other.
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency
Hallucination detection is a critical step toward understanding the trustworthiness of modern language models (LMs).
ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler
Numerical reasoning over text is a challenging task of Artificial Intelligence (AI), requiring reading comprehension and numerical reasoning abilities.
Ranked #1 on
Math Word Problem Solving
on MathQA
VRSO: Visual-Centric Reconstruction for Static Object Annotation
As a part of the perception results of intelligent driving systems, static object detection (SOD) in 3D space provides crucial cues for driving environment understanding.
DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models
Evaluating the quality and variability of text generated by Large Language Models (LLMs) poses a significant, yet unresolved research challenge.
Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild
To address this issue, we propose a novel approach called Marior (Margin Removal and \Iterative Content Rectification).
Atomic structure generation from reconstructing structural fingerprints
These methods would help identify or, in the case of generative models, even create novel crystal structures of materials with a set of specified functional properties to then be synthesized or isolated in the laboratory.
A Novel Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization
Standard ES methods with $d$-dimensional Gaussian smoothing suffer from the curse of dimensionality due to the high variance of Monte Carlo (MC) based gradient estimators.
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished.
GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving
Yet, their proficiency in tackling geometry math problems, which necessitates an integrated understanding of both textual and visual information, has not been thoroughly evaluated.
PBGen: Partial Binarization of Deconvolution-Based Generators for Edge Intelligence
This work explores the binarization of the deconvolution-based generator in a GAN for memory saving and speedup of image construction.
Power-Law Graph Cuts
Despite strong performance for a number of clustering tasks, spectral graph cut algorithms still suffer from several limitations: first, they require the number of clusters to be known in advance, but this information is often unknown a priori; second, they tend to produce clusters with uniform sizes.
Robust data-driven approach for predicting the configurational energy of high entropy alloys
In this study, a robust data-driven framework based on Bayesian approaches is proposed and demonstrated on the accurate and efficient prediction of configurational energy of high entropy alloys.
Learning nonlinear level sets for dimensionality reduction in function approximation
We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data.
Functional Analysis
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient.
Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics
In this paper, we propose a thermodynamic consistent neural network (TCNN) approach to build a data-driven model of the TSR with sparse experimental data.
A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models
However, the approach requires a sampling path to compute the pathwise gradient of the MI lower bound with respect to the design variables, and such a pathwise gradient is usually inaccessible for implicit models.
A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models
However, the approach in Kleinegesse et al., 2020 requires a pathwise sampling path to compute the gradient of the MI lower bound with respect to the design variables, and such a pathwise sampling path is usually inaccessible for implicit models.
Deep Online Correction for Monocular Visual Odometry
Second, the poses predicted by CNNs are further improved by minimizing photometric errors via gradient updates of poses during inference phases.
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates
Federated Learning (FL) enables multiple distributed clients (e. g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the client.
AutoNF: Automated Architecture Optimization of Normalizing Flows Using a Mixture Distribution Formulation
Although various flow models based on different transformations have been proposed, there still lacks a quantitative analysis of performance-cost trade-offs between different flows as well as a systematic way of constructing the best flow architecture.
On the Stochastic Stability of Deep Markov Models
Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems.
Simulation Intelligence: Towards a New Generation of Scientific Methods
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
Road-aware Monocular Structure from Motion and Homography Estimation
The Depth-CNN and Pose-CNN estimate dense depth map and ego-motion respectively, solving SFM, while the Pose-CNN and Ground-CNN followed by a homography layer solve the ground plane estimation problem.
Speech Privacy Leakage from Shared Gradients in Distributed Learning
Distributed machine learning paradigms, such as federated learning, have been recently adopted in many privacy-critical applications for speech analysis.
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense
In this paper, we seek to reconcile utility and privacy in FL by proposing a user-configurable privacy defense, RecUP-FL, that can better focus on the user-specified sensitive attributes while obtaining significant improvements in utility over traditional defenses.
M$^{6}$Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis
Document layout analysis is a crucial prerequisite for document understanding, including document retrieval and conversion.
DocAligner: Annotating Real-world Photographic Document Images by Simply Taking Pictures
Furthermore, considering the importance of fine-grained elements in document images, we present a details recurrent refinement module to enhance the output in a high-resolution space.
PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback
In this paper, we propose a novel RRTF (Rank Responses to align Test&Teacher Feedback) framework, which can effectively and efficiently boost pre-trained large language models for code generation.
Ranked #32 on
Code Generation
on HumanEval
On the Quantification of Image Reconstruction Uncertainty without Training Data
This, in turn, facilitates a probabilistic interpretation of observational data for decision-making.
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models
The performance of optical character recognition (OCR) heavily relies on document image quality, which is crucial for automatic document processing and document intelligence.
Advancing Urban Renewal: An Automated Approach to Generating Historical Arcade Facades with Stable Diffusion Models
Urban renewal and transformation processes necessitate the preservation of the historical urban fabric, particularly in districts known for their architectural and historical significance.
UPOCR: Towards Unified Pixel-Level OCR Interface
Without bells and whistles, the experimental results showcase that the proposed method can simultaneously achieve state-of-the-art performance on three tasks with a unified single model, which provides valuable strategies and insights for future research on generalist OCR models.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning
Specifically, we utilize constrained optimization to enforce local fairness on the client side and adopt a fairness-aware clustering-based aggregation on the server to further ensure the global model fairness across different sensitive groups while maintaining high utility.
GAPS: Geometry-Aware Problem Solver
By leveraging these improvements, GAPS showcases remarkable performance in resolving geometry math problems.
PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models
Crafting an ideal prompt for Large Language Models (LLMs) is a challenging task that demands significant resources and expert human input.
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
Uncertainty estimation is a crucial aspect of deploying dependable deep learning models in safety-critical systems.
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Motivated by this gap, we introduce a novel UQ method, sampling with perturbation for UQ (SPUQ), designed to tackle both aleatoric and epistemic uncertainties.
