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Found 50 papers, 27 papers with code
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness
Safe and reliable natural language inference is critical for extracting insights from clinical trial reports but poses challenges due to biases in large pre-trained language models.
Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets
Medical image segmentation presents the challenge of segmenting various-size targets, demanding the model to effectively capture both local and global information.
Rethinking Multi-domain Generalization with A General Learning Objective
Multi-domain generalization (mDG) is universally aimed to minimize the discrepancy between training and testing distributions to enhance marginal-to-label distribution mapping.
Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering
Whilst spectral Graph Neural Networks (GNNs) are theoretically well-founded in the spectral domain, their practical reliance on polynomial approximation implies a profound linkage to the spatial domain.
Diff-Oracle: Diffusion Model for Oracle Character Generation with Controllable Styles and Contents
To train Diff-Oracle effectively, we propose to obtain pixel-level paired oracle character images (i. e., style and content images) by a pre-trained image-to-image translation model.
Unraveling Batch Normalization for Realistic Test-Time Adaptation
While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to inaccurate target estimation.
Graph Neural Networks with Diverse Spectral Filtering
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their local contexts.
Polar-Doc: One-Stage Document Dewarping with Multi-Scope Constraints under Polar Representation
While Cartesian coordinates are typically leveraged by state-of-the-art approaches to learn a group of deformation control points, such representation is not efficient for dewarping model to learn the deformation information.
Semantic-aware Data Augmentation for Text-to-image Synthesis
In particular, we propose to augment texts in the semantic space via an Implicit Textual Semantic Preserving Augmentation ($ITA$), in conjunction with a specifically designed Image Semantic Regularization Loss ($L_r$) as Generated Image Semantic Conservation, to cope well with semantic mismatch and collapse.
Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges
To this end, we propose a more realistic and challenging scenario named open-pose 3D zero-shot classification, focusing on the recognition of 3D objects regardless of their orientation.
Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding
In the era of the Internet of Things (IoT), the retrieval of relevant medical information has become essential for efficient clinical decision-making.
Context Does Matter: End-to-end Panoptic Narrative Grounding with Deformable Attention Refined Matching Network
The current state-of-the-art methods first refine the representation of phrase by aggregating the most similar $k$ image pixels, and then match the refined text representations with the pixels of the image feature map to generate segmentation results.
MathAttack: Attacking Large Language Models Towards Math Solving Ability
Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems.
A Symbolic Character-Aware Model for Solving Geometry Problems
For the diagram encoder, we pre-train it under a multi-label classification framework with the symbolic characters as labels.
Learning by Analogy: Diverse Questions Generation in Math Word Problem
We then feed them to a question generator together with the scenario to obtain the corresponding diverse questions, forming a new MWP with a variety of questions and equations.
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection
We then design a novel patch-wise residual module in the anomaly learning head to extract and assess the fine-grained anomaly features from each sample, facilitating the learning of discriminative representations of anomaly instances.
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training
By lowering the volatility of the stock recommendation model, SVAT effectively reduces investment risks and outperforms state-of-the-art baselines by more than 30% in terms of risk-adjusted profits.
Exploring Numerical Priors for Low-Rank Tensor Completion with Generalized CP Decomposition
This work attempts to construct a new methodological framework called GCDTC (Generalized CP Decomposition Tensor Completion) for leveraging numerical priors and achieving higher accuracy in tensor completion.
Towards Better Robustness against Common Corruptions for Unsupervised Domain Adaptation
Recent studies have investigated how to achieve robustness for unsupervised domain adaptation (UDA).
Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation
On the one hand, the unidirectional projection enforces our model focused more on the core task, i. e., 3D segmentation; on the other hand, unlocking the bidirectional to unidirectional projection enables a deeper cross-domain semantic alignment and enjoys the flexibility to fuse better and complicated features from very different spaces.
Deep Learning for Brain Age Estimation: A Systematic Review
In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data.
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation
Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets.
SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation
Generating consistent and high-quality images from given texts is essential for visual-language understanding.
Rebalanced Zero-shot Learning
Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes.
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables
While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in the present continuous methods.
Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization
Due to the uneven distribution of offset points, these existing methods can hardly cluster all instance points.
Ranked #3 on
3D Instance Segmentation
on S3DIS
Outpainting by Queries
Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision.
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting
While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily.
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation
This combination of global and local alignment can precisely localize the crucial regions in segmentation target while preserving the overall semantic consistency.
From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion
While weakly supervised multi-view face reconstruction (MVR) is garnering increased attention, one critical issue still remains open: how to effectively fuse multiple image information to reconstruct high-precision 3D models.
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies
In this paper, we present a comprehensive survey trying to offer a systematic and structured investigation on robust adversarial training in pattern recognition.
Towards Simple and Accurate Human Pose Estimation with Stair Network
In this paper, we focus on tackling the precise keypoint coordinates regression task.
Generalised Image Outpainting with U-Transformer
In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalised image outpainting problem.
PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification
Automatic nuclei segmentation and classification play a vital role in digital pathology.
Ranked #4 on
Multi-tissue Nucleus Segmentation
on CoNSeP
Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing
Sentence-based Image Editing (SIE) aims to deploy natural language to edit an image.
Perturbation Diversity Certificates Robust Generalisation
It is possibly due to the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way, so that the adversarial samples are highly biased towards the decision boundary, resulting in an inhomogeneous data distribution.
AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training
We also propose a novel training algorithm able to align the disentangled content in the latent space to reduce micro-level lossy transformation.
Improving Model Robustness with Latent Distribution Locally and Globally
The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner.
Disentangling Semantic-to-visual Confusion for Zero-shot Learning
However, the traditional TL cannot search reliable unseen disentangled representations due to the unavailability of unseen classes in ZSL.
Structure First Detail Next: Image Inpainting with Pyramid Generator
Recent deep generative models have achieved promising performance in image inpainting.
Reborn Mechanism: Rethinking the Negative Phase Information Flow in Convolutional Neural Network
This paper proposes a novel nonlinear activation mechanism typically for convolutional neural network (CNN), named as reborn mechanism.
FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity
%on how to exploit strong convexity to further improve the convergence rate of AdaBelief.
LGD-GCN: Local and Global Disentangled Graph Convolutional Networks
}, we introduce a novel Local and Global Disentangled Graph Convolutional Network (LGD-GCN) to capture both local and global information for graph disentanglement.
Partial Differential Equations is All You Need for Generating Neural Architectures -- A Theory for Physical Artificial Intelligence Systems
In this work, we generalize the reaction-diffusion equation in statistical physics, Schr\"odinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research.
Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation
In particular, we show that the distribution discrepancy can be reduced by constraining feature gradients of two domains to have similar distributions.
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction
We propose a continuous neural network architecture, termed Explainable Tensorized Neural Ordinary Differential Equations (ETN-ODE), for multi-step time series prediction at arbitrary time points.
Global-aware Beam Search for Neural Abstractive Summarization
A global scoring mechanism is then developed to regulate beam search to generate summaries in a near-global optimal fashion.
W-Net : One-Shot Arbitrary-StyleChinese Character Generationwith Deep Neural Networks
Due to the huge category number, the sophisticated com-binations of various strokes and radicals, and the free writing or print-ing styles, generating Chinese characters with diverse styles is alwaysconsidered as a difficult task.
A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting
Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built.
Robust Metric Learning by Smooth Optimization
Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints.
