Search Results for author:
Found 26 papers, 11 papers with code
CURSOR: Scalable Mixed-Order Hypergraph Matching with CUR Decomposition
To achieve greater accuracy, hypergraph matching algorithms require exponential increases in computational resources.
Domain Generalization with Small Data
In this work, we propose to tackle the problem of domain generalization in the context of \textit{insufficient samples}.
Joint Sparse Representations and Coupled Dictionary Learning in Multi-Source Heterogeneous Image Pseudo-color Fusion
Considering that Coupled Dictionary Learning (CDL) method can obtain a reasonable linear mathematical relationship between resource images, we propose a novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-color fusion method.
SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training
Moreover, these methods ignore how to utilize the fine-grained dependencies among different skeleton joints to pre-train an efficient skeleton sequence learning model that can generalize well across different datasets.
Visual Causal Scene Refinement for Video Question Answering
Our VCSR involves two essential modules: i) the Question-Guided Refiner (QGR) module, which refines consecutive video frames guided by the question semantics to obtain more representative segment features for causal front-door intervention; ii) the Causal Scene Separator (CSS) module, which discovers a collection of visual causal and non-causal scenes based on the visual-linguistic causal relevance and estimates the causal effect of the scene-separating intervention in a contrastive learning manner.
Robust Cross-domain CT Image Reconstruction via Bayesian Noise Uncertainty Alignment
Under this probabilistic framework, we propose to alleviate the noise distribution shifts between source and target domains via implicit noise modeling schemes in the latent space and image space, respectively.
Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations
Empirical evaluation on a range of dynamic graph representation learning tasks demonstrates the superiority of our proposed approach compared to the baselines.
SelfME: Self-Supervised Motion Learning for Micro-Expression Recognition
To overcome this limitation, we proposed a novel MER framework using self-supervised learning to extract facial motion for ME (SelfME).
Micro Expression Recognition
Micro-Expression Recognition
+2
Graph Neural Network and Superpixel Based Brain Tissue Segmentation (Corrected Version)
Convolutional neural networks (CNNs) are usually used as a backbone to design methods in biomedical image segmentation.
Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection
Based on the finding that multiple different modules in image acquisition will lead to different sensitivity inconsistencies to the convolutional neural network (CNN)-based rendering in images, we propose a deep texture rendering module for texture difference enhancement and discriminative texture representation.
Causal Reasoning Meets Visual Representation Learning: A Prospective Study
Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing.
Learning with Signatures
We develop a closed-form equation to compute probably good optimal scale factors, as well as the formulation to obtain them by optimization.
Signature and Log-signature for the Study of Empirical Distributions Generated with GANs
In this paper, we bring forward the use of the recently developed Signature Transform as a way to measure the similarity between image distributions and provide detailed acquaintance and extensive evaluations.
Ranked #1 on
Image Generation
on AFHQ Wild
(RMSE Signature metric)
Orthonormal Product Quantization Network for Scalable Face Image Retrieval
Experiments are conducted on four commonly-used face datasets under both seen and unseen identities retrieval settings.
Instance segmentation with the number of clusters incorporated in embedding learning
Semantic and instance segmentation algorithms are two general yet distinct image segmentation solutions powered by Convolution Neural Network.
Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrieval
Recently, deep classwise hashing introduced a classwise loss supervised by class labels information alternatively; however, we find it still has its drawback.
Human Expression Recognition using Facial Shape Based Fourier Descriptors Fusion
Dynamic facial expression recognition has many useful applications in social networks, multimedia content analysis, security systems and others.
Learning Kernel for Conditional Moment-Matching Discrepancy-based Image Classification
KLN can simultaneously learn a more expressive kernel and label prediction distribution, thus, it can be used to improve the classification performance in both supervised and semi-supervised learning scenarios.
Unsupervised Domain Adaptation via Discriminative Manifold Propagation
Second, batch-wise training of deep learning limits the characterization of the global structure.
Discriminative Residual Analysis for Image Set Classification with Posture and Age Variations
Consequently, the crucial point of image set recognition is to mine the intrinsic connection or structural information from the image batches with variations.
Domain Adaptation and Image Classification via Deep Conditional Adaptation Network
In this paper, we consider a more general application scenario where the label distributions of the source and target domains are not the same.
RetinaMask: A Face Mask detector
In this paper, we propose RetinaFaceMask, which is a high-accuracy and efficient face mask detector.
Orthogonal Nonnegative Tucker Decomposition
In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD).
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
Deep hashing models have been proposed as an efficient method for large-scale similarity search.
Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss
In this regard, we propose a novel deep supervised hashing model to learn more compact class-level similarity preserving binary codes.
Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval
Deep distance metric learning (DDML), which is proposed to learn image similarity metrics in an end-to-end manner based on the convolution neural network, has achieved encouraging results in many computer vision tasks.$L2$-normalization in the embedding space has been used to improve the performance of several DDML methods.
