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Found 19 papers, 5 papers with code
All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)
This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan.
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.
A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selection
In this paper, we develop a lightweight video anomaly detection model.
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.
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.
All in One: Multi-task Prompting for Graph Neural Networks
Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks.
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.
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.
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.
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.
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.
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.
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.
