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Open Vocabulary Attribute Detection
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Open-Vocabulary Attribute Detection (OVAD) is a task that aims to detect and recognize an open set of objects and their associated attributes in an image. The objects and attributes are defined by text queries during inference, without prior knowledge of the tested classes during training.
논문
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LOWA: Localize Objects in the Wild with Attributes
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
OvarNet: Towards Open-vocabulary Object Attribute Recognition
Reproducible scaling laws for contrastive language-image learning
Open-vocabulary Attribute Detection
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
Localized Vision-Language Matching for Open-vocabulary Object Detection
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
Learning Transferable Visual Models From Natural Language Supervision
Open-Vocabulary Object Detection Using Captions
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