25 citations · 147 across the 70 of their papers we have counts for
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Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
Jiangmeng Li, Yifan Jin, Hang Gao +3
Graph contrastive learning (GCL) aims to align the positive features while differentiating the negative features in the latent space by minimizing a pair-wise contrastive loss. As…
Hacking Task Confounder in Meta-Learning
Jingyao Wang, Yi Ren, Zeen Song +3
Meta-learning enables rapid generalization to new tasks by learning knowledge from various tasks. It is intuitively assumed that as the training progresses, a model will acquire ri…
Background Debiased SAR Target Recognition via Causal Interventional Regularizer
Hongwei Dong, Fangzhou Han, Lingyu Si +2
Recent studies have utilized deep learning (DL) techniques to automatically extract features from synthetic aperture radar (SAR) images, which shows great promise for enhancing the…
Unleash Model Potential: Bootstrapped Meta Self-supervised Learning
Jingyao Wang, Zeen Song, Wenwen Qiang +1
The long-term goal of machine learning is to learn general visual representations from a small amount of data without supervision, mimicking three advantages of human cognition: i)…
Information Theory-Guided Heuristic Progressive Multi-View Coding
Jiangmeng Li, Hang Gao, Wenwen Qiang +1
Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to differe…
Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
Yujie Zhou, Wenwen Qiang, Anyi Rao +3
Zero-shot skeleton-based action recognition aims to recognize actions of unseen categories after training on data of seen categories. The key is to build the connection between vis…
Spatio-Temporal Branching for Motion Prediction using Motion Increments
Jiexin Wang, Yujie Zhou, Wenwen Qiang +3
Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature…
Towards the Sparseness of Projection Head in Self-Supervised Learning
Zeen Song, Xingzhe Su, Jingyao Wang +3
In recent years, self-supervised learning (SSL) has emerged as a promising approach for extracting valuable representations from unlabeled data. One successful SSL method is contra…
Towards Task Sampler Learning for Meta-Learning
Jingyao Wang, Wenwen Qiang, Xingzhe Su +3
Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing ta…
Unbiased Image Synthesis via Manifold Guidance in Diffusion Models
Xingzhe Su, Daixi Jia, Fengge Wu +3
Diffusion Models are a potent class of generative models capable of producing high-quality images. However, they often inadvertently favor certain data attributes, undermining the…
A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning
Lingyu Si, Hongwei Dong, Wenwen Qiang +5
Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark…
Manifold Constraint Regularization for Remote Sensing Image Generation
Xingzhe Su, Changwen Zheng, Wenwen Qiang +4
Generative Adversarial Networks (GANs) have shown notable accomplishments in remote sensing domain. However, this paper reveals that their performance on remote sensing images fall…
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design Anywhere
Jingyao Wang, Yuxuan Yang, Wenwen Qiang +2
Meta-learning, also known as ``learning to learn'', enables models to acquire great generalization abilities by learning from various tasks. Recent advancements have made these mod…
Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation
Xingzhe Su, Wenwen Qiang, Jie Hu +3
Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, a…
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
Hang Gao, Jiangmeng Li, Wenwen Qiang +5
Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from…