Search Results for author:
Found 39 papers, 9 papers with code
Topology-Preserving Class-Incremental Learning
A well-known issue for class-incremental learning is the catastrophic forgetting phenomenon, where the network's recognition performance on old classes degrades severely when incrementally learning new classes.
I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning
Online task-free continual learning (OTFCL) is a more challenging variant of continual learning which emphasizes the gradual shift of task boundaries and learns in an online mode.
Few-shot Online Anomaly Detection and Segmentation
Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where, post-deployment of the model, unlabeled data containing both normal and abnormal samples can be utilized to enhance the model's performance.
CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning
Besides, to address the classifier bias towards the new classes, we propose a novel approach to generate the pseudo-features to correct the classifier.
ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
We present ARTrackV2, which integrates two pivotal aspects of tracking: determining where to look (localization) and how to describe (appearance analysis) the target object across video frames.
Ranked #1 on
Visual Object Tracking
on NeedForSpeed
Knowledge Restore and Transfer for Multi-label Class-Incremental Learning
Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied.
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning
Deep neural networks suffer from catastrophic forgetting in class incremental learning, where the classification accuracy of old classes drastically deteriorates when the networks learn the knowledge of new classes.
Autoregressive Visual Tracking
We present ARTrack, an autoregressive framework for visual object tracking.
Ranked #1 on
Visual Tracking
on TNL2K
Deep Class Incremental Learning from Decentralized Data
In this paper, we focus on a new and challenging decentralized machine learning paradigm in which there are continuous inflows of data to be addressed and the data are stored in multiple repositories.
Scene-Adaptive Attention Network for Crowd Counting
First of all, we design a deformable attention in-built Transformer backbone, which learns adaptive feature representations with deformable sampling locations and dynamic attention weights.
Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay -- 3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A Continual Object Classification
Online continual learning in the wild is a very difficult task in machine learning.
Class incremental learning for video action classification
Class Incremental Learning (CIL) is a hot topic in machine learning for CNN models to learn new classes incrementally.
Anomaly Detection via Self-organizing Map
Anomaly detection plays a key role in industrial manufacturing for product quality control.
Ranked #46 on
Anomaly Detection
on MVTec AD
Direct Measure Matching for Crowd Counting
Second, we derive a semi-balanced form of Sinkhorn divergence, based on which a Sinkhorn counting loss is designed for measure matching.
Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration
In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation.
Towards a Universal Model for Cross-Dataset Crowd Counting
This paper proposes to handle the practical problem of learning a universal model for crowd counting across scenes and datasets.
Few-Shot Class-Incremental Learning
FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones.
Ranked #8 on
Few-Shot Class-Incremental Learning
on CIFAR-100
(Average Accuracy metric)
Beyond Universal Person Re-ID Attack
We propose a \emph{more universal} adversarial perturbation (MUAP) method for both image-agnostic and model-insensitive person Re-ID attack.
Bayesian Loss for Crowd Count Estimation with Point Supervision
In crowd counting datasets, each person is annotated by a point, which is usually the center of the head.
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification
The reason is that the bilinear feature matrix is sensitive to the magnitudes and correlations of local CNN feature elements which can be measured by its singular values.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
+1
Transductive Semi-Supervised Deep Learning using Min-Max Features
In this paper, we propose Transductive Semi-Supervised Deep Learning (TSSDL) method that is effective for training Deep Convolutional Neural Network (DCNN) models.
Discriminative Feature Learning with Foreground Attention for Person Re-Identification
Specifically, a novel foreground attentive subnetwork is designed to drive the network's attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons.
Kernelized Subspace Pooling for Deep Local Descriptors
Experimental results on several patch matching benchmarks show that our method outperforms the state-of-the-arts significantly.
Graph Matching via Multiplicative Update Algorithm
As a fundamental problem in computer vision, graph matching problem can usually be formulated as a Quadratic Programming (QP) problem with doubly stochastic and discrete (integer) constraints.
Deep Self-Paced Learning for Person Re-Identification
In this paper, we propose a novel deep self-paced learning (DSPL) algorithm to alleviate this problem, in which we apply a self-paced constraint and symmetric regularization to help the relative distance metric training the deep neural network, so as to learn the stable and discriminative features for person Re-ID.
Tracking Persons-of-Interest via Unsupervised Representation Adaptation
Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically different in multiple shots due to significant variations in scale, pose, expression, illumination, and make-up.
Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification
The class-identity term keeps the intra-class samples within each camera view gathering together, the relative distance term maximizes the distance between the intra-class class set and inter-class set across different camera views, and the regularization term smoothness the parameters of deep convolutional neural network (CNN).
Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification
The proposed method can take full advantages of the structured distance relationships among these training samples, with the constructed complete graph.
Point to Set Similarity Based Deep Feature Learning for Person Re-Identification
One of the key issues for deep learning based person Re-ID is the selection of proper similarity comparison criteria, and the performance of learned features using existing criterion based on pairwise similarity is still limited, because only P2P distances are mostly considered.
Single Image Super Resolution - When Model Adaptation Matters
The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors.
Person Re-Identification by Multi-Channel Parts-Based CNN With Improved Triplet Loss Function
Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras.
Superpixel Hierarchy
Quantitative and qualitative evaluation on a number of computer vision applications was conducted, demonstrating that the proposed method is the top performer.
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
Single modality action recognition on RGB or depth sequences has been extensively explored recently.
Salient Object Detection: A Discriminative Regional Feature Integration Approach
Our method, which is based on multi-level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score.
Learning to Search Efficiently in High Dimensions
Traditional approaches relied on algorithmic constructions that are often data independent (such as Locality Sensitive Hashing) or weakly dependent (such as kd-trees, k-means trees).
Nonlinear Learning using Local Coordinate Coding
This paper introduces a new method for semi-supervised learning on high dimensional nonlinear manifolds, which includes a phase of unsupervised basis learning and a phase of supervised function learning.
Stochastic Relational Models for Large-scale Dyadic Data using MCMC
Stochastic relational models provide a rich family of choices for learning and predicting dyadic data between two sets of entities.
Deep Learning with Kernel Regularization for Visual Recognition
In this paper we focus on training deep neural networks for visual recognition tasks.
Predictive Matrix-Variate t Models
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements.
