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Zero-Shot Object Detection
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Zero-shot object detection (ZSD) is the task of object detection where no visual training data is available for some of the target object classes.
<span style="color:grey; opacity: 0.6">( Image credit: Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety
Finding the Reflection Point: Unpadding Images to Remove Data Augmentation Artifacts in Large Open Source Image Datasets for Machine Learning
The Power of One: A Single Example is All it Takes for Segmentation in VLMs
LangGas: Introducing Language in Selective Zero-Shot Background Subtraction for Semi-Transparent Gas Leak Detection with a New Dataset
CP-DETR: Concept Prompt Guide DETR Toward Stronger Universal Object Detection
No Annotations for Object Detection in Art through Stable Diffusion
Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3D Objects
DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding
OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion
Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO
Eating Smart: Advancing Health Informatics with the Grounding DINO based Dietary Assistant App
OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy
DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM
Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head
Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection
YOLO-World: Real-Time Open-Vocabulary Object Detection
Multimodal Data Curation via Object Detection and Filter Ensembles
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection
Zero-Shot Visual Classification with Guided Cropping
ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data
Meta-ZSDETR: Zero-shot DETR with Meta-learning
Scaling Open-Vocabulary Object Detection
Multi-modal Queried Object Detection in the Wild
DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping
ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection
Efficient Feature Distillation for Zero-shot Annotation Object Detection
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline
Resolving Semantic Confusions for Improved Zero-Shot Detection
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting
On Hyperbolic Embeddings in 2D Object Detection
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