One-Shot Object Detection
10 papers with code • 2 benchmarks • 2 datasets
( Image credit: Siamese Mask R-CNN )
Benchmarks
These leaderboards are used to track progress in One-Shot Object Detection
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MS COCO
SKU110K
Latest papers with no code
Adaptive Base-class Suppression and Prior Guidance Network for One-Shot Object Detection
One-shot object detection (OSOD) aims to detect all object instances towards the given category specified by a query image.
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents.
Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection
Moreover, the trained model is successful in identifying the GW170817 event in the LIGO H1 detector data.
Semantic-aligned Fusion Transformer for One-shot Object Detection
One-shot object detection aims at detecting novel objects according to merely one given instance.
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.
Adaptive Image Transformer for One-Shot Object Detection
One-shot object detection tackles a challenging task that aims at identifying within a target image all object instances of the same class, implied by a query image patch.
CAT: Cross-Attention Transformer for One-Shot Object Detection
Given a query patch from a novel class, one-shot object detection aims to detect all instances of that class in a target image through the semantic similarity comparison.
FOC OSOD: Focus on Classification One-Shot Object Detection
This paper analyzes the serious false positive problem in OSOD and proposes a Focus on Classification One-Shot Object Detection (FOC OSOD) framework, which is improved in two important aspects: (1) classification cascade head with the fixed IoU threshold can enhance the robustness of classification by comparing multiple close regions; (2) classification region deformation on the query feature and the reference feature to obtain a more effective comparison region.
A Broad Dataset is All You Need for One-Shot Object Detection
We here show that this generalization gap can be nearly closed by increasing the number of object categories used during training.
