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Found 50 papers, 17 papers with code
Learning over Knowledge-Base Embeddings for Recommendation
In this work, we propose to reason over knowledge base embeddings for personalized recommendation.
Left-Right Comparative Recurrent Model for Stereo Matching
Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view.
A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images
This report summarises our method and validation results for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1: Lesion Segmentation.
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.
Dynamic Feature Generation Network for Answer Selection
Extracting appropriate features to represent a corpus is an important task for textual mining.
A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning
Detecting scene text of arbitrary shapes has been a challenging task over the past years.
Ranked #18 on
Scene Text Detection
on ICDAR 2015
Multi-task Learning with Coarse Priors for Robust Part-aware Person Re-identification
MPN has three key advantages: 1) it does not need to conduct body part detection in the inference stage; 2) its model is very compact and efficient for both training and testing; 3) in the training stage, it requires only coarse priors of body part locations, which are easy to obtain.
Ranked #5 on
Person Re-Identification
on CUHK03 detected
The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing
By analyzing the benefits and limitations of DGF research, we try to identify gaps in current research, meanwhile, reveal new research opportunities, and suggest areas for further investigation.
Cryptography and Security
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with Gated Recurrent Unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations.
Batch Coherence-Driven Network for Part-aware Person Re-Identification
First, we introduce a batch coherence-guided channel attention (BCCA) module that highlights the relevant channels for each respective part from the output of a deep backbone model.
Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
Top-N item recommendation has been a widely studied task from implicit feedback.
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Specifically, LAFE utilizes the region attention modules and channel attention modules to extract discriminative features and confusable features respectively.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.
Beating the Standard Quantum Limit under Ambient Conditions with Solid-State Spins
However, it has not been realized in solid-state spin systems at ambient conditions, owing to its intrinsic complexity for the preparation and survival of pure and entangled quantum states.
Quantum Physics
IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning
This paper studies how to improve the generalization performance and learning speed of the navigation agents trained with deep reinforcement learning (DRL).
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency.
Ranked #1 on
Scene Text Detection
on ICDAR 2015
(Accuracy metric)
Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification
In particular, the performance of our unsupervised UCF method in the MSMT17$\to$Market1501 task is better than that of the fully supervised setting on Market1501.
Neural-IMLS: Self-supervised Implicit Moving Least-Squares Network for Surface Reconstruction
We conducted extensive experiments on various benchmarks, including synthetic scans and real scans.
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy
In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i. e., early stopping (ES) strategy and reward-level interactive (RI) strategy.
Quality-aware Part Models for Occluded Person Re-identification
Existing approaches typically rely on outside tools to infer visible body parts, which may be suboptimal in terms of both computational efficiency and ReID accuracy.
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate.
Ranked #2 on
Entity Alignment
on DBP1M DE-EN
Style Interleaved Learning for Generalizable Person Re-identification
This common practice causes the model to overfit to existing feature styles in the source domain, resulting in sub-optimal generalization ability on target domains.
Graph Soft-Contrastive Learning via Neighborhood Ranking
Moreover, as the neighborhood size exponentially expands with more hops considered, we propose neighborhood sampling strategies to improve learning efficiency.
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations.
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML).
Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translation
In this paper, we propose a simple but effective method called "Candidate Soups," which can obtain high-quality translations while maintaining the inference speed of NAT models.
Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection
Furthermore, we reconstruct feature selection solutions using these embeddings and select the feature subset with the highest performance for downstream tasks as the optimal subset.
Sharpness-Aware Gradient Matching for Domain Generalization
In this paper, we present two conditions to ensure that the model could converge to a flat minimum with a small loss, and present an algorithm, named Sharpness-Aware Gradient Matching (SAGM), to meet the two conditions for improving model generalization capability.
Modeling Entities as Semantic Points for Visual Information Extraction in the Wild
As the first contribution of this work, we curate and release a new dataset for VIE, in which the document images are much more challenging in that they are taken from real applications, and difficulties such as blur, partial occlusion, and printing shift are quite common.
Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning
Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently.
DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation
The Class Incremental Semantic Segmentation (CISS) extends the traditional segmentation task by incrementally learning newly added classes.
MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching
Traditional unsupervised EM assumes that all entities come from two tables; however, it is more common to match entities from multiple tables in practical applications, that is, multi-table entity matching (multi-table EM).
TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution
Moreover, our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods.
Exploring the Intersection of Complex Aesthetics and Generative AI for Promoting Cultural Creativity in Rural China after the Post-Pandemic Era
This paper explores using generative AI and aesthetics to promote cultural creativity in rural China amidst COVID-19's impact.
Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning
Subsequently, we utilize the defined query-aware temporal paths on a history temporal graph to model historical path information related to queries for reasoning.
A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge
A vector database is used to store high-dimensional data that cannot be characterized by traditional DBMS.
OpenSD: Unified Open-Vocabulary Segmentation and Detection
To address these challenges, we present a universal transformer-based framework, abbreviated as OpenSD, which utilizes the same architecture and network parameters to handle open-vocabulary segmentation and detection tasks.
Ranked #8 on
Zero Shot Segmentation
on Segmentation in the Wild
D3Former: Jointly Learning Repeatable Dense Detectors and Feature-enhanced Descriptors via Saliency-guided Transformer
Notably, tests on 3DLoMatch, even with a low overlap ratio, show that our method consistently outperforms recently published approaches such as RoReg and RoITr.
Text2MDT: Extracting Medical Decision Trees from Medical Texts
In this work, we propose a novel task, Text2MDT, to explore the automatic extraction of MDTs from medical texts such as medical guidelines and textbooks.
Delayed finite-dimensional observer-based control of 2D linear parabolic PDEs
To compensate the output delay that appears in the infinite-dimensional part of the closed-loop system, for the first time for delayed PDEs we suggest a vector Lyapunov functional combined with the recently introduced vector Halanay inequality.
Left-right Discrepancy for Adversarial Attack on Stereo Networks
Stereo matching neural networks often involve a Siamese structure to extract intermediate features from left and right images.
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations
To tackle the above issues, we propose a neural Spatial-Temporal Delay Differential Equation model, namely STDDE.
scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation
Despite the inherent limitations of existing Large Language Models in directly reading and interpreting single-cell omics data, they demonstrate significant potential and flexibility as the Foundation Model.
Question Calibration and Multi-Hop Modeling for Temporal Question Answering
In the real world, many facts contained in KGs are time-constrained thus temporal KGQA has received increasing attention.
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing
Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios.
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts.
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding
Addressing these limitations, we introduce scCDCG (single-cell RNA-seq Clustering via Deep Cut-informed Graph), a novel framework designed for efficient and accurate clustering of scRNA-seq data that simultaneously utilizes intercellular high-order structural information.
UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning
To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs.
PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification
The other is to classification on feature distribution of visual tokens from vision encoder.
NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvature
Despite recent advances in reconstructing an organic model with the neural signed distance function (SDF), the high-fidelity reconstruction of a CAD model directly from low-quality unoriented point clouds remains a significant challenge.
