PAPER REVIEW

• Generative Adversarial Networks
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• Conditional Generative Adversarial Nets
• pix2pix
• StarGANs
• Contrastive Learning for Unpaired Image-to-Image Translation
• ViT
• MLP-Mixer
• Towards General Purpose Vision Systems (GPV)
• SelfPatch
• Towards Total Recall in Industrial Anomaly Detection (PatchCore)
• Emerging Properties of Self-Supervised Vision Transformers (DINO)
• SimCLR
• MoCo
• MoCoV3
• BYOL
• SwAV
• What do Self-Supervised ViT learn?
• MAE
• iBOT
• UniAD
• MetaFormer
• Unified-IO
• BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
• LARGE LANGUAGE MODELS ARE HUMAN-LEVEL PROMPT ENGINEERS
• Learning Transferable Visual Models From Natural Language Supervision
• WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
• Instruction Induction: From Few Examples to Natural Language Task Descriptions
• OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
• Optimizing Prompts for Text-to-Image Generation
• Grounded Language-Image Pre-training
• Universal Few-shot Learning Of Dense Prediction Tasks With Viual Token Matching
• Anomaly Detection Requires Better Representations
• Towards Open World Object Detection
• Flamingo: a Visual Language Model for Few-Shot Learning
• Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
• Learning to Prompt for Vision-Language Models
• Conditional Prompt Learning For Vision-Language Models
• POUF: Prompt-oriented Unsupervised Fine-tuning For Large Pre-trained Models
• SimpleNet: A Simple Network for Image Anomaly Detection and Localization