Contrastive Learning
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Benchmarks
Most implemented
A Simple Framework for Contrastive Learning of Visual Representations
Momentum Contrast for Unsupervised Visual Representation Learning
Improved Baselines with Momentum Contrastive Learning
Supervised Contrastive Learning
SimCSE: Simple Contrastive Learning of Sentence Embeddings
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Papers
Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal
Adverse weather image restoration aims to recover images degraded by rain, haze, snow, and other weather-induced artifacts, thereby improving the robustness of outdoor vision systems. Existing unified restoration models …
Contrastive LearningImage RestorationCross-Lingual Alignment for Decoder-Only Models using MoE Routers
Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is not possible in decoder-only LLMs because of varying multilingual toke…
Cross-Lingual TransferContrastive LearningSentZero: An Enhanced Sentence-Centric Vision-Language Pretraining for Multi-Task Zero-Shot Chest X-Ray Analysis
Vision-language (VL) pretraining using paired chest X-ray (CXR) images and radiology reports has shown strong potential for medical image understanding. However, existing methods often remain dependent on task-specific f…
Zero-shot GeneralizationContrastive LearningType-IV Code Clone Detection via Layer-Wise Non-Contrastive Representation Learning
Software clones are fragments of code that are similar or functionally equivalent to each other. They pose significant challenges for maintenance, refactoring, and bug detection. Detecting Type-IV clones, which are seman…
Representation LearningContrastive LearningRegRet: Enhancing Region-Level Retrieval in Large Multimodal Models
Region-level retrieval aims to align user-specified image regions with relevant regions or textual descriptions, playing a crucial role in realworld applications such as e-commerce product search and RAG. Although recent…
Contrastive LearningEfficient Quantization-Aware Distillation with Cross-Modal Alignment for Edge Vision-Language Models
Large-scale vision-language models (VLM) such as CLIP enable strong open-vocabulary reasoning, yet deploying these capabilities on resource-constrained edge devices remains challenging. EdgeVL addresses this problem by d…
Contrastive Learning