Papers Self-Supervised Image Classification
“Self-Supervised Image Classification” 태그가 달린 논문 110편 · 필터 해제
Learning by Sorting: Self-supervised Learning with Group Ordering Constraints
Contrastive learning has become an important tool in learning representations from unlabeled data mainly relying on the idea of minimizing distance between positive data pairs, e.g., views from the same images, and maxim…
Contrastive LearningSelf-Supervised Image ClassificationSelf-Supervised LearningImproving Visual Representation Learning through Perceptual Understanding
We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features. We do this by: (i) the introduction of…
Representation LearningSelf-Supervised Image ClassificationMasked Reconstruction Contrastive Learning with Information Bottleneck Principle
Contrastive learning (CL) has shown great power in self-supervised learning due to its ability to capture insight correlations among large-scale data. Current CL models are biased to learn only the ability to discriminat…
Contrastive Learningimage-classificationImage ClassificationSelf-Supervised Image Classification+2EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text alig…
Action ClassificationAction RecognitionImage ClassificationInstance Segmentation+8Towards Sustainable Self-supervised Learning
Although increasingly training-expensive, most self-supervised learning (SSL) models have repeatedly been trained from scratch but not fully utilized, since only a few SOTAs are employed for downstream tasks. In this wor…
Object DetectionRelationSelf-Supervised Image ClassificationSelf-Supervised Learning+1Exploring Target Representations for Masked Autoencoders
Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the masked portion according to the target …
Image ClassificationInstance SegmentationKnowledge Distillationobject-detection+5BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, most existing studies operate on low-level image pixels, which hi…
image-classificationImage ClassificationKnowledge DistillationRepresentation Learning+2Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains. However, the most common InfoNCE-based methods suffer from some dilemmas, such as \textit{uniformity-tolerance dilemma} (…
Contrastive LearningGraph Representation LearningRepresentation LearningSelf-Supervised Image Classification+2Bootstrapped Masked Autoencoders for Vision BERT Pretraining
We propose bootstrapped masked autoencoders (BootMAE), a new approach for vision BERT pretraining. BootMAE improves the original masked autoencoders (MAE) with two core designs: 1) momentum encoder that provides online f…
DecoderObject DetectionPredictionSelf-Supervised Image Classification+1Unsupervised Visual Representation Learning by Synchronous Momentum Grouping
In this paper, we propose a genuine group-level contrastive visual representation learning method whose linear evaluation performance on ImageNet surpasses the vanilla supervised learning. Two mainstream unsupervised lea…
ClusteringContrastive LearningLinear evaluationRepresentation Learning+1Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
Masked image modeling, an emerging self-supervised pre-training method, has shown impressive success across numerous downstream vision tasks with Vision transformers. Its underlying idea is simple: a portion of the input…
Image ClassificationInstance SegmentationObject DetectionSelf-Supervised Image Classification+2Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder
Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation an…
Cell DetectionContrastive LearningImage ReconstructionSegmentation+2Masked Siamese Networks for Label-Efficient Learning
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the representation of an image view containing randomly masked patches to the repres…
image-classificationImage ClassificationSelf-Supervised Image ClassificationSelf-Supervised Learning+1mc-BEiT: Multi-choice Discretization for Image BERT Pre-training
Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a classification task with a visual vocabulary,…
Instance Segmentationobject-detectionObject DetectionRepresentation Learning+3Mugs: A Multi-Granular Self-Supervised Learning Framework
In self-supervised learning, multi-granular features are heavily desired though rarely investigated, as different downstream tasks (e.g., general and fine-grained classification) often require different or multi-granular…
Contrastive LearningSelf-Supervised Image ClassificationSelf-Supervised LearningSemantic Segmentation+1CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning
As a representative self-supervised method, contrastive learning has achieved great successes in unsupervised training of representations. It trains an encoder by distinguishing positive samples from negative ones given …
Contrastive LearningSelf-Supervised Image ClassificationWeak Augmentation Guided Relational Self-Supervised Learning
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most methods mainly focus on the instance leve…
Contrastive LearningRelationSelf-Supervised Image ClassificationSelf-Supervised LearningVision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images. Applied to ImageNet, this leads…
Action ClassificationAction RecognitionCopy DetectionDomain Generalization+13OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization. We propose OFA, a Task-Agnostic and Modality-Agnostic framework that supports …
Image Captioningimage-classificationImage ClassificationImage Generation+14Context Autoencoder for Self-Supervised Representation Learning
We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. We pretrain an encoder by making predictions in the encoded representation space. The pr…
DecoderInstance Segmentationobject-detectionObject Detection+4