Zero-Shot Object Detection
26 papers with code • 7 benchmarks • 6 datasets
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.
( Image credit: Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts )
Benchmarks
These leaderboards are used to track progress in Zero-Shot Object Detection
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Libraries
Use these libraries to find Zero-Shot Object Detection models and implementations
MS COCO
LVIS
PASCAL VOC 2007
MSCOCO
RF100
Latest papers with no code
DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM
We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini.
Multimodal Data Curation via Object Detection and Filter Ensembles
We propose an approach for curating multimodal data that we used for our entry in the 2023 DataComp competition filtering track.
Zero-Shot Visual Classification with Guided Cropping
We empirically show that our approach improves zero-shot classification results across architectures and datasets, favorably for small objects.
Meta-ZSDETR: Zero-shot DETR with Meta-learning
Zero-shot object detection aims to localize and recognize objects of unseen classes.
Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline
Methods for object detection and segmentation often require abundant instance-level annotations for training, which are time-consuming and expensive to collect.
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting
Our approach is motivated by the high-level conceptual similarity of (metric-based) meta-learning and prompt-based learning to learn generalizable few-shot and zero-shot object detection models respectively without fine-tuning.
On Hyperbolic Embeddings in 2D Object Detection
Object detection, for the most part, has been formulated in the euclidean space, where euclidean or spherical geodesic distances measure the similarity of an image region to an object class prototype.
A Survey of Deep Learning for Low-Shot Object Detection
Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task.
Zero-shot detection of daily objects in YCB video dataset
In this work, we explore the zero-shot detection of daily objects in indoor scenes since the objects’ size and environment are closely related to the manufacturing setup.
Semantics-Guided Contrastive Network for Zero-Shot Object detection
To address these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection.
