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
T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy
Recognizing the complementary strengths and weaknesses of both text and visual prompts, we introduce T-Rex2 that synergizes both prompts within a single model through contrastive learning.
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection
lancezpf/seeds
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To tackle this, we propose the Semantic Separable Diffusion Synthesizer (SeeDS) framework for Zero-Shot Food Detection (ZSFD).
ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data
stanfordmimi/villa
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ICCV 2023
The first key contribution of this work is to demonstrate through systematic evaluations that as the pairwise complexity of the training dataset increases, standard VLMs struggle to learn region-attribute relationships, exhibiting performance degradations of up to 37% on retrieval tasks.
Scaling Open-Vocabulary Object Detection
google-research/scenic
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NeurIPS 2023
However, with OWL-ST, we can scale to over 1B examples, yielding further large improvement: With an L/14 architecture, OWL-ST improves AP on LVIS rare classes, for which the model has seen no human box annotations, from 31. 2% to 44. 6% (43% relative improvement).
Multi-modal Queried Object Detection in the Wild
yifanxu74/mq-det
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NeurIPS 2023
To address the learning inertia problem brought by the frozen detector, a vision conditioned masked language prediction strategy is proposed.
DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping
AnasIbrahim/image_agnostic_segmentation
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In this work, we are interested in open sets where the number of classes is unknown, varying, and without pre-knowledge about the objects' types.
ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection
casia-iva-lab/zbs
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CVPR 2023
However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories.
Efficient Feature Distillation for Zero-shot Annotation Object Detection
dragonlzm/EZSD
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We propose a new setting for detecting unseen objects called Zero-shot Annotation object Detection (ZAD).
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion.
Resolving Semantic Confusions for Improved Zero-Shot Detection
sandipan211/ZSD-SC-Resolver
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British Machine Vision Conference 2022
Zero-shot detection (ZSD) is a challenging task where we aim to recognize and localize objects simultaneously, even when our model has not been trained with visual samples of a few target ("unseen") classes.
