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Found 46 papers, 15 papers with code
Weighted Motion Averaging for the Registration of Multi-View Range Scans
With the overlapping percentage available, it views the overlapping percentage as the corresponding weight of each scan pair and proposes the weight motion averaging algorithm, which can pay more attention to reliable and accurate relative motions.
Effective scaling registration approach by imposing the emphasis on the scale factor
This paper proposes an effective approach for the scaling registration of $m$-D point sets.
An Effective Approach for Point Clouds Registration Based on the Hard and Soft Assignments
For the registration of partially overlapping point clouds, this paper proposes an effective approach based on both the hard and soft assignments.
Simultaneous merging multiple grid maps using the robust motion averaging
The main idea of this approach is to recover all global motions for map merging from a set of relative motions.
Multi-view Registration Based on Weighted Low Rank and Sparse Matrix Decomposition of Motions
Then, it proposes the weighted LRS decomposition, where each block element is assigned with one estimated weight to denote its reliability.
K-means clustering for efficient and robust registration of multi-view point sets
Subsequently, the shape comprised by all cluster centroids is used to sequentially estimate the rigid transformation for each point set.
Adaptive Co-weighting Deep Convolutional Features For Object Retrieval
Aggregating deep convolutional features into a global image vector has attracted sustained attention in image retrieval.
Multi-view registration of unordered range scans by fast correspondence propagation of multi-scale descriptors
This paper proposes a global approach for the multi-view registration of unordered range scans.
Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval
We show that by considering each deep feature as a heat source, our unsupervised aggregation method is able to avoid over-representation of \emph{bursty} features.
Spatio-Temporal Road Scene Reconstruction using Superpixel Markov Random Field
The data fidelity term in the MRF's energy function is jointly computed according to the superpixel features of color, texture and location.
Feature Concatenation Multi-view Subspace Clustering
To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data.
Constrained Bilinear Factorization Multi-view Subspace Clustering
Multi-view clustering is an important and fundamental problem.
Visual Space Optimization for Zero-shot Learning
The other is to optimize the visual feature structure in an intermediate embedding space, and in this method we successfully devise a multilayer perceptron framework based algorithm that is able to learn the common intermediate embedding space and meanwhile to make the visual data structure more distinctive.
Domain segmentation and adjustment for generalized zero-shot learning
In the generalized zero-shot learning, synthesizing unseen data with generative models has been the most popular method to address the imbalance of training data between seen and unseen classes.
Registration of multi-view point sets under the perspective of expectation-maximization
Registration of multi-view point sets is a prerequisite for 3D model reconstruction.
Consistent and Complementary Graph Regularized Multi-view Subspace Clustering
The complementary graph regularizer investigates the specific information of multiple views.
Generalized Label Enhancement with Sample Correlations
Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances.
Robust Motion Averaging under Maximum Correntropy Criterion
Recently, the motion averaging method has been introduced as an effective means to solve the multi-view registration problem.
S2IGAN: Speech-to-Image Generation via Adversarial Learning
An estimated half of the world's languages do not have a written form, making it impossible for these languages to benefit from any existing text-based technologies.
Bidirectional Loss Function for Label Enhancement and Distribution Learning
More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one.
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics
To thisend, we present a novel method for the unsupervised domain adaptationin histopathological image analysis, based on a backbone for embeddinginput images into a feature space, and a graph neural layer for propa-gating the supervision signals of images with labels.
Contrastive Learning
Histopathological Image Classification
+3
Frame-wise Cross-modal Matching for Video Moment Retrieval
Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.
Multi-view Hierarchical Clustering
This paper focuses on the multi-view clustering, which aims to promote clustering results with multi-view data.
Multi-view Subspace Clustering Networks with Local and Global Graph Information
Furthermore, underlying graph information of multi-view data is always ignored in most existing multi-view subspace clustering methods.
Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering
It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint.
Show and Speak: Directly Synthesize Spoken Description of Images
This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes.
Effective multi-view registration of point sets based on student's t mixture model
What's more, the t-distribution takes the noise with heavy-tail into consideration, which makes the proposed method be inherently robust to noises and outliers.
3DMNDT:3D multi-view registration method based on the normal distributions transform
Under the NDT framework, this paper proposes a novel multi-view registration method, named 3D multi-view registration based on the normal distributions transform (3DMNDT), which integrates the K-means clustering and Lie algebra solver to achieve multi-view registration.
AnyoneNet: Synchronized Speech and Talking Head Generation for Arbitrary Person
In this paper, we present an automatic method to generate synchronized speech and talking-head videos on the basis of text and a single face image of an arbitrary person as input.
Dynamic Slimmable Denoising Network
Here, we present dynamic slimmable denoising network (DDS-Net), a general method to achieve good denoising quality with less computational complexity, via dynamically adjusting the channel configurations of networks at test time with respect to different noisy images.
C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image
Our method is motivated by two cause-effect chains including category-causality chain and anatomy-causality chain.
3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation
Here, we make the first attempt to achieve generic text-guided cross-category 3D object generation via a new 3D-TOGO model, which integrates a text-to-views generation module and a views-to-3D generation module.
MCoCo: Multi-level Consistency Collaborative Multi-view Clustering
Further, we designed a multi-level consistency collaboration strategy, which utilizes the consistent information of semantic space as a self-supervised signal to collaborate with the cluster assignments in feature space.
Ranked #1 on
Multiview Clustering
on Fashion-MNIST
Multi-view Semantic Consistency based Information Bottleneck for Clustering
In this way, the learned semantic consistency from multi-view data can improve the information bottleneck to more exactly distinguish the consistent information and learn a unified feature representation with more discriminative consistent information for clustering.
Semantically Consistent Multi-view Representation Learning
In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner.
Label Information Bottleneck for Label Enhancement
In this work, we focus on the challenging problem of Label Enhancement (LE), which aims to exactly recover label distributions from logical labels, and present a novel Label Information Bottleneck (LIB) method for LE.
DualGenerator: Information Interaction-based Generative Network for Point Cloud Completion
To tackle these challenges, we propose an information interaction-based generative network for point cloud completion ($\mathbf{DualGenerator}$).
Contrastive Label Enhancement
Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity.
Overlap Bias Matching is Necessary for Point Cloud Registration
Specifically, we propose a plug-and-play Overlap Bias Matching Module (OBMM) comprising two integral components, overlap sampling module and bias prediction module.
Evaluation and Analysis of Hallucination in Large Vision-Language Models
In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework.
DBDNet:Partial-to-Partial Point Cloud Registration with Dual Branches Decoupling
Point cloud registration plays a crucial role in various computer vision tasks, and usually demands the resolution of partial overlap registration in practice.
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration
Specifically, we first incorporate the projected images from the point clouds and fuse the cross-modal features using the attention mechanism.
Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor
Assuming that all clients have a single shared sample for each class, the knowledge anchor is constructed before each local training stage by extracting shared samples for missing classes and randomly selecting one sample per class for non-dominant classes.
Unsupervised Temporal Action Localization via Self-paced Incremental Learning
Thereafter, we design two (constant- and variable- speed) incremental instance learning strategies for easy-to-hard model training, thus ensuring the reliability of these video pseudolabels and further improving overall localization performance.
Iterative Feedback Network for Unsupervised Point Cloud Registration
In this paper, we propose a novel Iterative Feedback Network (IFNet) for unsupervised point cloud registration, in which the representation of low-level features is efficiently enriched by rerouting subsequent high-level features.
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models
Inspired by this observation, we propose the "Assembled Projection Heads" (APH) method for enhancing the reliability of federated models.
