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Found 13 papers, 9 papers with code
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues.
Open-Vocabulary Calibration for Vision-Language Models
Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few.
Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition
However, the high computational cost of optimization-based TTA algorithms makes it intractable to run on resource-constrained edge devices.
Comprehensive Competition Mechanism in Palmprint Recognition
The traditional competition mechanism focuses solely on selecting the winner of different channels without considering the spatial information of the features.
Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification
Subsequently, we introduce anchor models for short- and long-spectrum, which constrain the optimization directions of local models associated with long- and short-spectrum images.
A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems
In addition, a Distributed Hierarchical Integration (DHI) algorithm is also designed for the global solution process.
Multi-stage image denoising with the wavelet transform
In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i. e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB).
A Survey on Incomplete Multi-view Clustering
However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods.
NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery
Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet).
Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in GANs
In the generator of typical Generative Adversarial Networks (GANs), a noise is inputted to generate fake samples via a series of convolutional operations.
Two-stage Image Classification Supervised by a Single Teacher Single Student Model
The samples of the candidate classes are utilized to learn a student classifier based on L2-minimization in the second stage.
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification
We propose a deep collaborative weight-based classification (DeepCWC) method to resolve this problem, by providing a novel option to fully take advantage of deep features in classic machine learning.
Dual Asymmetric Deep Hashing Learning
The deep hash functions are then learned through two networks by minimizing the gap between the learned features and discrete codes.
