Search Results for author:
Found 58 papers, 12 papers with code
Learning Real Facial Concepts for Independent Deepfake Detection
Meanwhile, IDC redefines the classification strategy by making independent decisions based on the concept of the real class and the presence of forgery artifacts.
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution
Deepfake attribution (DFA) aims to perform multiclassification on different facial manipulation techniques, thereby mitigating the detrimental effects of forgery content on the social order and personal reputations.
Concept Factorization via Self-Representation and Adaptive Graph Structure Learning
Concept Factorization (CF) models have attracted widespread attention due to their excellent performance in data clustering.
Stronger, Steadier & Superior: Geometric Consistency in Depth VFM Forges Domain Generalized Semantic Segmentation
In each layer of the VFMs, we incorporate depth-aware learnable tokens to continuously decouple domain-invariant visual and spatial information, thereby enhancing depth awareness and attention of the VFMs.
Exploiting Diffusion Prior for Real-World Image Dehazing with Unpaired Training
Inspired by the strong generative capabilities of diffusion models in producing both hazy and clear images, we exploit diffusion prior for real-world image dehazing, and propose an unpaired framework named Diff-Dehazer.
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution
The advent of local continuous image function (LIIF) has garnered significant attention for arbitrary-scale super-resolution (SR) techniques.
Breaking Fine-Grained Classification Barriers with Cost-Free Data in Few-Shot Class-Incremental Learning
Current fine-grained classification research mainly concentrates on fine-grained feature learning, but in real-world applications, the bigger issue often lies in the data.
NeRF Inpainting with Geometric Diffusion Prior and Balanced Score Distillation
The limitations manifest in two critical aspects: the inadequate capture of geometric information by pretrained diffusion models and the suboptimal guidance provided by existing Score Distillation Sampling (SDS) methods.
Optimized Cryo-CMOS Technology with VTH<0.2V and Ion>1.2mA/um for High-Peformance Computing
We report the design-technology co-optimization (DTCO) scheme to develop a 28-nm cryogenic CMOS (Cryo-CMOS) technology for high-performance computing (HPC).
End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial
However, existing approaches loosely consider these relationships and mechanisms by a non-end-to-end learning framework, resulting in sub-optimal feature extractions and fusions for CD.
EVC-MF: End-to-end Video Captioning Network with Multi-scale Features
To address these issues, we propose an end-to-end encoder-decoder-based network (EVC-MF) for video captioning, which efficiently utilizes multi-scale visual and textual features to generate video descriptions.
Robust Input Shaping Vibration Control via Extended Kalman Filter-Incorporated Residual Neural Network
Its main ideas are two-fold: a) adopting an extended Kalman filter to address a vertical flexible beam's model errors; and b) adopting a residual neural network to cascade with the extended Kalman filter for eliminating the remaining observation errors.
Texture-guided Coding for Deep Features
This image reconstruction network leverages features and texture images to reconstruct preview images for humans.
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential Recommendation
To the end, in this paper, we propose a novel model named Multi-level Sequence Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential recommendation.
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion
In the second stage, we integrate a degradation-aware module into a simplified ControlNet, enabling flexible adaptation to various degradations based on the learned representations.
Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model
The performance of an LFA model relies heavily on its training process, which is a non-convex optimization.
Bias Mitigating Few-Shot Class-Incremental Learning
Some recent methods somewhat alleviate the accuracy imbalance between base and incremental classes by fine-tuning the feature extractor in the incremental sessions, but they further cause the accuracy imbalance between past and current incremental classes.
class-incremental learning
Few-Shot Class-Incremental Learning
+1
Characteristics Matching Based Hash Codes Generation for Efficient Fine-grained Image Retrieval
Firstly the limited information embedding capacity of low-dimensional binary hash codes coupled with the detailed information required to describe fine-grained categories results in a contradiction in feature learning.
Generating Human-Centric Visual Cues for Human-Object Interaction Detection via Large Vision-Language Models
To capitalize on these rich Human-Centric Visual Cues, we propose a novel approach named HCVC for HOI detection.
Federated Class-Incremental Learning with Prompting
For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid).
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis
Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a linear bias incorporation (LBI) scheme is important in present the training overshooting and fluctuation, as well as preventing the model from premature convergence.
On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement
We tackle this issue by examining the spectral discriminators in the context of perceptual image super-resolution (i. e., GAN-based SR), as SR image quality is susceptible to spectral changes.
Image Super-Resolution
No-Reference Image Quality Assessment
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition
Then, based on the idea of stacking ensemble, long short-term memory is employed as an error correction module to forecast the components separately, and the forecast results are treated as new features to be fed into extreme gradient boosting for the second-step forecasting.
Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks
An Undirected Weighted Network (UWN) is commonly found in big data-related applications.
Multi-constrained Symmetric Nonnegative Latent Factor Analysis for Accurately Representing Large-scale Undirected Weighted Networks
An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e. g., a protein interaction network from a bioinformatics application.
Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution
Recent learning-based approaches have achieved significant progress in light field (LF) image super-resolution (SR) by exploring convolution-based or transformer-based network structures.
LaCon: Late-Constraint Diffusion for Steerable Guided Image Synthesis
To offer more controllability for the generation process, existing studies, termed as early-constraint methods in this paper, leverage extra conditions and incorporate them into pre-trained diffusion models.
Ranked #1 on
Conditional Text-to-Image Synthesis
on COCO 2017 val
Conditional Image Generation
Conditional Text-to-Image Synthesis
Online Sparse Streaming Feature Selection Using Adapted Classification
Traditional feature selections need to know the feature space before learning, and online streaming feature selection (OSFS) is proposed to process streaming features on the fly.
A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection
Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning.
Constraint-Induced Symmetric Nonnegative Matrix Factorization for Accurate Community Detection
Motivated by this discovery, this paper proposes a novel Constraintinduced Symmetric Nonnegative Matrix Factorization (C-SNMF) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent feature matrices, thus preserving its representation learning capacity; b) Incorporating a symmetry-regularizer into its objective function, which preserves the symmetry of the learnt low-rank approximation to the adjacency matrix, thereby making the resultant detector precisely illustrate the target network’s symmetry; and c) Introducing a graph-regularizer that preserves local invariance of the network’s intrinsic geometry into its learning objective, thus making the achieved detector well-aware of community structure within the target network.
Multi-queue Momentum Contrast for Microvideo-Product Retrieval
The booming development and huge market of micro-videos bring new e-commerce channels for merchants.
Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction
The idea of the proposed MMA is mainly two-fold: 1) apply different $L_p$-norm on loss function and regularization to form different variant models in different metric spaces, and 2) aggregate these variant models.
Surrogate-assisted level-based learning evolutionary search for heat extraction optimization of enhanced geothermal system
To solve this issue, a novel surrogate-assisted level-based learning evolutionary search algorithm (SLLES) is proposed for heat extraction optimization of enhanced geothermal system.
A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs
Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module; b) Building the residual connection and weighted representation propagation to obtain high representation capacity; and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG.
A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images
Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality.
Generic Cryo-CMOS Device Modeling and EDACompatible Platform for Reliable Cryogenic IC Design
Furthermore, by incorporating the Cryo-CMOS compact model into the process design kit (PDK), the cryogenic 4 Kb SRAM, 5-bit flash ADC and 8-bit current steering DAC are designed, and their performance is readily investigated and optimized on the EDA-compatible platform, hence laying a solid foundation for large-scale cryogenic IC design.
FedVMR: A New Federated Learning method for Video Moment Retrieval
As the first that is explored in VMR field, the new task is defined as video moment retrieval with distributed data.
Prototype-Based Layered Federated Cross-Modal Hashing
And due to the problems of statistical heterogeneity, model heterogeneity, and forcing each client to accept the same parameters, applying federated learning to cross-modal hash learning becomes very tricky.
Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization
In this work, a novel and efficient data-driven evolutionary algorithm, called generalized data-driven differential evolutionary algorithm (GDDE), is proposed to reduce the number of simulation runs on well-placement and control optimization problems.
PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis
A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes.
Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices
A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system.
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data
High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications.
Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval
To narrow the domain differences between sketches and images, we extract edge maps for natural images and treat them as a bridge between images and sketches, which have similar content to images and similar style to sketches.
An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model
An alternating-direction-method-based nonnegative latent factor model can perform efficient representation learning to a high-dimensional and incomplete (HDI) matrix.
A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data
High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications.
Adaptive Divergence-based Non-negative Latent Factor Analysis
High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative for representing the inherent non-negativity of node interactions.
ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator
Recently, several Vision Transformer (ViT) based methods have been proposed for Fine-Grained Visual Classification (FGVC). These methods significantly surpass existing CNN-based ones, demonstrating the effectiveness of ViT in FGVC tasks. However, there are some limitations when applying ViT directly to FGVC. First, ViT needs to split images into patches and calculate the attention of every pair, which may result in heavy redundant calculation and unsatisfying performance when handling fine-grained images with complex background and small objects. Second, a standard ViT only utilizes the class token in the final layer for classification, which is not enough to extract comprehensive fine-grained information.
High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection
Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circle.
Second-order Symmetric Non-negative Latent Factor Analysis
Precise representation of large-scale undirected network is the basis for understanding relations within a massive entity set.
Asymmetric Modality Translation For Face Presentation Attack Detection
In this paper, we propose a novel framework based on asymmetric modality translation for face presentation attack detection in bi-modality scenarios.
Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data
With the vigorous development of multimedia equipment and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic.
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation
It is desirable to transfer the knowledge stored in a well-trained source model onto non-annotated target domain in the absence of source data.
Generalized Nesterov's Acceleration-incorporated Non-negative and Adaptive Latent Factor Analysis
A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service applications.
Far-Field Super-Resolution Imaging By Nonlinear Excited Evanescent Waves
Abbe's resolution limit, one of the best-known physical limitations, poses a great challenge for any wave systems in imaging, wave transport, and dynamics.
Super-Resolution
Optics
Weakly-Supervised Online Hashing
However, existing hashing methods for social image retrieval are based on batch mode which violates the nature of social images, i. e., social images are usually generated periodically or collected in a stream fashion.
Dual Graph Representation Learning
Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications.
BIGSAGE: unsupervised inductive representation learning of graph via bi-attended sampling and global-biased aggregating
Different kinds of representation learning techniques on graph have shown significant effect in downstream machine learning tasks.
Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation
The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding.
