Papers Self-Supervised Image Classification
“Self-Supervised Image Classification” 태그가 달린 논문 110편 · 필터 해제
When Do Flat Minima Optimizers Work?
Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers. …
BenchmarkingGraph LearningGraph Representation LearningImage Classification+9Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classification benchmark, limiting their applicabilit…
image-classificationImage ClassificationLinear evaluationRepresentation Learning+4Max-Margin Contrastive Learning
Standard contrastive learning approaches usually require a large number of negatives for effective unsupervised learning and often exhibit slow convergence. We suspect this behavior is due to the suboptimal selection of …
Contrastive LearningRepresentation LearningSelf-Supervised Image ClassificationMasked Feature Prediction for Self-Supervised Visual Pre-Training
We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models. Our approach first randomly masks out a portion of the input sequence and then predicts the feature of the masked regions.…
Action ClassificationAction RecognitionPredictionSelf-Supervised Image ClassificationSimilarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning
Contrastive representation learning has proven to be an effective self-supervised learning method. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as posi…
Contrastive LearningLinear evaluationRepresentation LearningSelf-Supervised Image Classification+3PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers
This paper explores a better prediction target for BERT pre-training of vision transformers. We observe that current prediction targets disagree with human perception judgment.This contradiction motivates us to learn a p…
Image Classificationobject-detectionObject DetectionPrediction+2SimMIM: A Simple Framework for Masked Image Modeling
This paper presents SimMIM, a simple framework for masked image modeling. We simplify recently proposed related approaches without special designs such as block-wise masking and tokenization via discrete VAE or clusterin…
Representation LearningSelf-Supervised Image ClassificationTask 2iBOT: Image BERT Pre-Training with Online Tokenizer
The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces. In this work, we study masked image…
image-classificationImage ClassificationInstance SegmentationLanguage Modeling+7Masked Autoencoders Are Scalable Vision Learners
This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is b…
DecoderDomain GeneralizationImage ClassificationObject Detection+4Self-Supervised Learning by Estimating Twin Class Distributions
We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a soft…
Fine-Grained Image ClassificationImage ClassificationRepresentation LearningSelf-Supervised Image Classification+4Weakly Supervised Contrastive Learning
Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent achievement of contrastive learning. Most of the existing contrastive learning frameworks ado…
Contrastive LearningRepresentation LearningSelf-Supervised Image ClassificationSemi-Supervised Image ClassificationCompressive Visual Representations
Learning effective visual representations that generalize well without human supervision is a fundamental problem in order to apply Machine Learning to a wide variety of tasks. Recently, two families of self-supervised m…
Contrastive LearningImage ClassificationLinear evaluationSelf-Supervised Image ClassificationReSSL: Relational Self-Supervised Learning with Weak Augmentation
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most of methods mainly focus on the instance l…
Contrastive LearningRelationSelf-Supervised Image ClassificationSelf-Supervised LearningXCiT: Cross-Covariance Image Transformers
Following their success in natural language processing, transformers have recently shown much promise for computer vision. The self-attention operation underlying transformers yields global interactions between all token…
image-classificationImage ClassificationInstance Segmentationobject-detection+3Efficient Self-supervised Vision Transformers for Representation Learning
This paper investigates two techniques for developing efficient self-supervised vision transformers (EsViT) for visual representation learning. First, we show through a comprehensive empirical study that multi-stage arch…
Representation LearningSelf-Supervised Image ClassificationBEiT: BERT Pre-Training of Image Transformers
We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose…
Document Image ClassificationDocument Layout Analysisimage-classificationImage Classification+2Unsupervised Visual Representation Learning by Online Constrained K-Means
Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to assign each instance a pseudo label that w…
ClusteringContrastive LearningImage ClusteringMetric Learning+5Divide and Contrast: Self-supervised Learning from Uncurated Data
Self-supervised learning holds promise in leveraging large amounts of unlabeled data, however much of its progress has thus far been limited to highly curated pre-training data such as ImageNet. We explore the effects of…
ClusteringContrastive LearningSelf-Supervised Image ClassificationSelf-Supervised LearningLarge-Scale Unsupervised Person Re-Identification with Contrastive Learning
Existing public person Re-Identification~(ReID) datasets are small in modern terms because of labeling difficulty. Although unlabeled surveillance video is abundant and relatively easy to obtain, it is unclear how to lev…
Contrastive LearningDomain Adaptationimage-classificationImage Classification+4VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial solution is obtained when the encoder out…
Representation LearningSelf-Supervised Image ClassificationSelf-Supervised LearningSemi-Supervised Image Classification