Search Results for author:
Found 29 papers, 12 papers with code
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.
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.
Data-Centric AI: Deep Generative Differentiable Feature Selection via Discrete Subsetting as Continuous Embedding Space Optimization
We generalize this problem into a deep differentiable feature selection task and propose a new perspective: discrete feature subsetting as continuous embedding space optimization.
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.
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).
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.
Graph Soft-Contrastive Learning via Neighborhood Ranking
In this paper, we investigate this research question and introduce a novel GCL paradigm, namely Graph Soft-Contrastive Learning (GSCL), which can conduct graph self-supervise learning via neighborhood ranking without relying on the binary-contrastive setting.
Style Interleaved Learning for Generalizable Person Re-identification
We employ the features of interleaved styles to update the feature extractor and classifiers using different forward propagations, which helps the model avoid overfitting to certain domain styles.
Domain Generalization
Generalizable Person Re-identification
+1
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.
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.
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.
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.
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.
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)
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).
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
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.
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.
Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
Top-N item recommendation has been a widely studied task from implicit feedback.
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.
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.
The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing
Most greybox fuzzing tools are coverage-guided as code coverage is strongly correlated with bug coverage.
Cryptography and Security
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
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
Dynamic Feature Generation Network for Answer Selection
Extracting appropriate features to represent a corpus is an important task for textual mining.
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.
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.
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.
Learning over Knowledge-Base Embeddings for Recommendation
In this work, we propose to reason over knowledge base embeddings for personalized recommendation.
