Weakly Supervised Object Detection
50 papers with code • 17 benchmarks • 13 datasets
Weakly Supervised Object Detection (WSOD) is the task of training object detectors with only image tag supervisions.
( Image credit: Soft Proposal Networks for Weakly Supervised Object Localization )
Benchmarks
These leaderboards are used to track progress in Weakly Supervised Object Detection
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Libraries
Use these libraries to find Weakly Supervised Object Detection models and implementationsDatasets
ImageNet
MS COCO
Charades
Foggy Cityscapes
HICO-DET
PASCAL VOC 2007
PASCAL VOC 2012 test
Clipart1k
Latest papers with no code
Sparse Generation: Making Pseudo Labels Sparse for weakly supervision with points
In recent years, research on point weakly supervised object detection (PWSOD) methods in the field of computer vision has attracted people's attention.
Weakly Supervised Open-Vocabulary Object Detection
Despite weakly supervised object detection (WSOD) being a promising step toward evading strong instance-level annotations, its capability is confined to closed-set categories within a single training dataset.
Text-image Alignment for Diffusion-based Perception
Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving.
Gall Bladder Cancer Detection from US Images with Only Image Level Labels
We posit that even when we have only the image level label, still formulating the problem as object detection (with bounding box output) helps a deep neural network (DNN) model focus on the relevant region of interest.
Read, look and detect: Bounding box annotation from image-caption pairs
Various methods have been proposed to detect objects while reducing the cost of data annotation.
Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection
Accurate Named Entity Recognition (NER) is crucial for various information retrieval tasks in industry.
Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information
In spite of intensive research on deep learning (DL) approaches over the past few years, there is still a significant performance gap between WSOD and fully supervised object detection.
DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection
Second, through our design, the object queries and the foreground query in the decoder share consensus on the class semantics, therefore making the strong and weak supervision mutually benefit each other for domain alignment.
Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection
However, since such approaches only utilize the highest score proposal and discard the potentially useful information from other proposals, their independent MIL backbone often limits models to salient parts of an object or causes them to detect only one object per class.
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth
We propose an amplifier method for enhancing the performance of WSOD by integrating depth information.
