SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 3451–3475 of 6661 papers

TitleStatusHype
VCM: Vision Concept Modeling Based on Implicit Contrastive Learning with Vision-Language Instruction Fine-Tuning—0
VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning—0
Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models—0
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning—0
Vi2CLR: Video and Image for Visual Contrastive Learning of Representation—0
Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis—0
Video Salient Object Detection via Contrastive Features and Attention Modules—0
Video sentence grounding with temporally global textual knowledge—0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—0
ViEEG: Hierarchical Neural Coding with Cross-Modal Progressive Enhancement for EEG-Based Visual Decoding—0
View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning—0
Explicit View-labels Matter: A Multifacet Complementarity Study of Multi-view Clustering—0
Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning—0
ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning—0
ViFi-ReID: A Two-Stream Vision-WiFi Multimodal Approach for Person Re-identification—0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification—0
ViLEM: Visual-Language Error Modeling for Image-Text Retrieval—0
Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition—0
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation—0
Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives—0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval—0
VisTA: Vision-Text Alignment Model with Contrastive Learning using Multimodal Data for Evidence-Driven, Reliable, and Explainable Alzheimer's Disease Diagnosis—0
Visual Commonsense based Heterogeneous Graph Contrastive Learning—0
Visual Encoding and Debiasing for CTR Prediction—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified