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Found 22 papers, 5 papers with code
SiCL: Silhouette-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change
In this paper, we address a highly challenging yet critical task: unsupervised long-term person re-identification with clothes change.
Ranked #1 on
Unsupervised Person Re-Identification
on PRCC
Clothes Changing Person Re-Identification
Contrastive Learning
+3
Unsupervised Long-Term Person Re-Identification with Clothes Change
We investigate unsupervised person re-identification (Re-ID) with clothes change, a new challenging problem with more practical usability and scalability to real-world deployment.
Clustering
Unsupervised Long Term Person Re-Identification
+2
Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification
The most popular approaches to tackle unsupervised person ReID are usually performing a clustering algorithm to yield pseudo labels at first and then exploit the pseudo labels to train a deep neural network.
Ranked #1 on
Unsupervised Person Re-Identification
on DukeMTMCreID
(MAP metric)
Learning a Self-Expressive Network for Subspace Clustering
We show that our SENet can not only learn the self-expressive coefficients with desired properties on the training data, but also handle out-of-sample data.
Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification
To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively.
Learning Graph Normalization for Graph Neural Networks
We conduct extensive experiments on benchmark datasets for different tasks, including node classification, link prediction, graph classification and graph regression, and confirm that the learned graph normalization leads to competitive results and that the learned weights suggest the appropriate normalization techniques for the specific task.
Is an Affine Constraint Needed for Affine Subspace Clustering?
Specifically, our analysis provides conditions that guarantee the correctness of affine subspace clustering methods both with and without the affine constraint, and shows that these conditions are satisfied for high-dimensional data.
Stochastic Sparse Subspace Clustering
State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points.
Convolutional Subspace Clustering Network with Block Diagonal Prior
Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points.
Self-Supervised Convolutional Subspace Clustering Network
However, the applicability of subspace clustering has been limited because practical visual data in raw form do not necessarily lie in such linear subspaces.
Ranked #2 on
Image Clustering
on Extended Yale-B
Online Learning Algorithms for Quaternion ARMA Model
In this paper, we address the problem of adaptive learning for autoregressive moving average (ARMA) model in the quaternion domain.
Distributed Variational Bayesian Algorithms for Extended Object Tracking
This paper is concerned with the problem of distributed extended object tracking, which aims to collaboratively estimate the state and extension of an object by a network of nodes.
On Geometric Analysis of Affine Sparse Subspace Clustering
In this paper, we develop a novel geometric analysis for a variant of SSC, named affine SSC (ASSC), for the problem of clustering data from a union of affine subspaces.
Constrained Sparse Subspace Clustering with Side-Information
Subspace clustering refers to the problem of segmenting high dimensional data drawn from a union of subspaces into the respective subspaces.
Density-Adaptive Kernel based Efficient Reranking Approaches for Person Reidentification
In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve efficient and effective reranking.
Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification
Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space.
Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework
In this paper, we propose a joint optimization framework --- Structured Sparse Subspace Clustering (S$^3$C) --- for learning both the affinity and the segmentation.
Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering
Our geometric analysis also provides a theoretical justification and a geometric interpretation for the balance between the connectedness (due to $\ell_2$ regularization) and subspace-preserving (due to $\ell_1$ regularization) properties for elastic net subspace clustering.
Ranked #7 on
Image Clustering
on coil-100
(Accuracy metric)
Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning
State of the art approaches for Semi-Supervised Learning (SSL) usually follow a two-stage framework -- constructing an affinity matrix from the data and then propagating the partial labels on this affinity matrix to infer those unknown labels.
Structured Sparse Subspace Clustering: A Unified Optimization Framework
Our framework is based on expressing each data point as a structured sparse linear combination of all other data points, where the structure is induced by a norm that depends on the unknown segmentation.
HEp-2 Cell Classification via Fusing Texture and Shape Information
Indirect Immunofluorescence (IIF) HEp-2 cell image is an effective evidence for diagnosis of autoimmune diseases.
Dynamic texture and scene classification by transferring deep image features
Moreover we explore two different implementations of the TCoF scheme, i. e., the \textit{spatial} TCoF and the \textit{temporal} TCoF, in which the mean-removed frames and the difference between two adjacent frames are used as the inputs of the ConvNet, respectively.
