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Found 44 papers, 15 papers with code
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation.
Molecular Property Prediction Based on Graph Structure Learning
Following that, we conduct graph structure learning on the MSG (i. e., molecule-level graph structure learning) to get the final molecular embeddings, which are the results of fusing both GNN encoded molecular representations and the relationships among molecules, i. e., combining both intra-molecule and inter-molecule information.
scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning
CICL consists of a Transformer encoder, a clustering head, a projection head and a contrastive loss module.
IDDR-NGP: Incorporating Detectors for Distractors Removal with Instant Neural Radiance Field
To validate the effectiveness and robustness of IDDR-NGP, we provide a wide range of distractors with corresponding annotated labels added to both realistic and synthetic scenes.
Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties
Extensive experiments with two benchmark datasets QM9 and ZINC250k show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fr\'echet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models.
Privacy-Preserving Face Recognition Using Random Frequency Components
The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals.
Meta-ZSDETR: Zero-shot DETR with Meta-learning
Zero-shot object detection aims to localize and recognize objects of unseen classes.
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution
Observing the quality issue of HR images, in this paper we propose a novel idea to boost STISR by first enhancing the quality of HR images and then using the enhanced HR images as supervision to do STISR.
Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings
The emergence of vertical federated learning (VFL) has stimulated concerns about the imperfection in privacy protection, as shared feature embeddings may reveal sensitive information under privacy attacks.
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
Compared with typical ST data (e. g., traffic data), ST ocean data is more complicated but with unique characteristics, e. g., diverse regionality and high sparsity.
Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer
On the one hand, standard retrieval systems are usually biased to common semantics and seldom exploit diversity-aware regularization in training, which makes it difficult to promote diversity by post-processing.
Molecular Property Prediction by Semantic-invariant Contrastive Learning
Contrastive learning have been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery.
Activity Cliff Prediction: Dataset and Benchmark
In this paper, we first introduce ACNet, a large-scale dataset for AC prediction.
GIPA: A General Information Propagation Algorithm for Graph Learning
Specifically, the bit-wise correlation calculates the element-wise attention weight through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method
In this paper, we propose and solve a new problem called hierarchical few-shot object detection (Hi-FSOD), which aims to detect objects with hierarchical categories in the FSOD paradigm.
Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery?
Self-supervised pre-training is gaining increasingly more popularity in AI-aided drug discovery, leading to more and more pre-trained models with the promise that they can extract better feature representations for molecules.
DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain
To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask.
C3-STISR: Scene Text Image Super-resolution with Triple Clues
In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution.
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification
However, to the best of our knowledge, most existing methods consider only either the diversity or the quality of augmented data, thus cannot fully mine the potential of DA for NLP.
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison
To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes.
Fairness Amidst Non-IID Graph Data: Current Achievements and Future Directions
The importance of understanding and correcting algorithmic bias in machine learning (ML) has led to an increase in research on fairness in ML, which typically assumes that the underlying data is independent and identically distributed (IID).
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection
For instance, studies have revealed that federated learning is vulnerable to backdoor attacks, whereby a compromised participant can stealthily modify the model's behavior in the presence of backdoor triggers.
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples
Extensive experiments on four standard SSL benchmarks show that DP-SSL can provide reliable labels for unlabeled data and achieve better classification performance on test sets than existing SSL methods, especially when only a small number of labeled samples are available.
Weakly-supervised Text Classification Based on Keyword Graph
Among them, keyword-driven methods are the mainstream where user-provided keywords are exploited to generate pseudo-labels for unlabeled texts.
Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction
A number of deep learning based algorithms have been proposed to recover high-quality videos from low-quality compressed ones.
ICDAR 2021 Competition on Scene Video Text Spotting
During the competition period (opened on 1st March, 2021 and closed on 11th April, 2021), a total of 24 teams participated in the three proposed tasks with 46 valid submissions, respectively.
Accurate Few-Shot Object Detection With Support-Query Mutual Guidance and Hybrid Loss
Most object detection methods require huge amounts of annotated data and can detect only the categories that appear in the training set.
Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information
Many deep learning based video compression artifact removal algorithms have been proposed to recover high-quality videos from low-quality compressed videos.
Federated Face Recognition
Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Text Recognition in Real Scenarios with a Few Labeled Samples
Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications.
Object-QA: Towards High Reliable Object Quality Assessment
In this paper, we define the problem of object quality assessment for the first time and propose an effective approach named Object-QA to assess high-reliable quality scores for object images.
Non-Local ConvLSTM for Video Compression Artifact Reduction
Video compression artifact reduction aims to recover high-quality videos from low-quality compressed videos.
REAPS: Towards Better Recognition of Fine-grained Images by Region Attending and Part Sequencing
Fine-grained image recognition has been a hot research topic in computer vision due to its various applications.
Learning Competitive and Discriminative Reconstructions for Anomaly Detection
Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier.
You Only Recognize Once: Towards Fast Video Text Spotting
Video text spotting is still an important research topic due to its various real-applications.
Global Semantic Consistency for Zero-Shot Learning
In this paper, we propose an end-to-end framework, called Global Semantic Consistency Network (GSC-Net for short), which makes complete use of the semantic information of both seen and unseen classes, to support effective zero-shot learning.
Edit Probability for Scene Text Recognition
The advantage lies in that the training process can focus on the missing, superfluous and unrecognized characters, and thus the impact of the misalignment problem can be alleviated or even overcome.
AON: Towards Arbitrarily-Oriented Text Recognition
Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts.
Ranked #9 on
Scene Text Recognition
on ICDAR 2003
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Focusing Attention: Towards Accurate Text Recognition in Natural Images
FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images.
Label Propagation on K-partite Graphs with Heterophily
In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption.
Robust Median Reversion Strategy for On-Line Portfolio Selection
Online portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades.
