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Found 34 papers, 13 papers with code
An End-to-End OCR Text Re-organization Sequence Learning for Rich-text Detail Image Comprehension
Nowadays rich description on detail images help users know more about the commodities.
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods.
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph.
Rethinking Propagation for Unsupervised Graph Domain Adaptation
Motivated by our empirical analysis, we reevaluate the role of GNNs in graph domain adaptation and uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains.
Less is More: A Closer Look at Semantic-based Few-Shot Learning
To address this, we propose a simple but effective framework for few-shot learning tasks, specifically designed to exploit the textual information and language model.
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis
Based on this fact, we introduce a simple partition mechanism to boost the performance of two INR methods for image reconstruction: one for learning INRs, and the other for learning-to-learn INRs.
Graph Neural Architecture Search with GPT-4
In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).
Homophily-enhanced Structure Learning for Graph Clustering
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results.
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR
To address these challenges, we introduce a novel methodology for the subgrade distress detection task by leveraging the multi-view information from 3D-GPR data.
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world.
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics
Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process.
LORE: Logical Location Regression Network for Table Structure Recognition
Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats.
Hilbert Distillation for Cross-Dimensionality Networks
Since the distilled 2D networks are supervised by the curves converted from dimensionally heterogeneous 3D features, the 2D networks are given an informative view in terms of learning structural information embedded in well-trained high-dimensional representations.
Dynamic Data-Free Knowledge Distillation by Easy-to-Hard Learning Strategy
Besides, CuDFKD adapts the generation target dynamically according to the status of student model.
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Efficient Medical Image Segmentation Based on Knowledge Distillation
To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network.
Distilling Holistic Knowledge with Graph Neural Networks
The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned by distilling the holistic knowledge in a contrastive manner.
Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data
Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data.
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation
Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc.
Cross-modal Image Retrieval with Deep Mutual Information Maximization
Prior work usually uses a three-stage strategy to tackle this task: 1) extract the features of the inputs; 2) fuse the feature of the source image and its modified text to obtain fusion feature; 3) learn a similarity metric between the desired image and the source image + modified text by using deep metric learning.
Cyclic Label Propagation for Graph Semi-supervised Learning
To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA.
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
The shared sub-structures between training classes and test classes are essential in few-shot graph classification.
Matching Text with Deep Mutual Information Estimation
Our approach, Text matching with Deep Info Max (TIM), is integrated with a procedure of unsupervised learning of representations by maximizing the mutual information between text matching neural network's input and output.
Hierarchical Graph Pooling with Structure Learning
HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.
Ranked #1 on
Graph Classification
on PROTEINS
HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding
Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.
Deep Style Match for Complementary Recommendation
In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper.
Navigation Objects Extraction for Better Content Structure Understanding
Dynamic and personalized elements such as top stories, recommended list in a webpage are vital to the understanding of the dynamic nature of web 2. 0 sites.
Relational Multi-Manifold Co-Clustering
In this study, we focus on improving the co-clustering performance via manifold ensemble learning, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces.
Weakly Supervised Multiclass Video Segmentation
In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation.
Semi-Supervised Coupled Dictionary Learning for Person Re-identification
In this paper, to bridge the human appearance variations across cameras, two coupled dictionaries that relate to the gallery and probe cameras are jointly learned in the training phase from both labeled and unlabeled images.
Probabilistic Graphlet Cut: Exploiting Spatial Structure Cue for Weakly Supervised Image Segmentation
Finally, we propose a novel image segmentation algorithm, called graphlet cut, that leverages the learned graphlet distribution in measuring the homogeneity of a set of spatially structured superpixels.
Semi-supervised Node Splitting for Random Forest Construction
Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples.
