Weakly Supervised Object Detection
51 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 )
Libraries
Use these libraries to find Weakly Supervised Object Detection models and implementationsDatasets
Latest papers
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentation
On the other hand, WSSS methods based on Vision Transformers (ViT) have not yet explored valid alternatives to CAM.
ImaginaryNet: Learning Object Detectors without Real Images and Annotations
Given a class label, the language model is used to generate a full description of a scene with a target object, and the text-to-image model deployed to generate a photo-realistic image.
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations.
Active Learning Strategies for Weakly-supervised Object Detection
On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency.
W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
Generally, with given pseudo ground-truths generated from the well-trained WSOD network, we propose a two-module iterative training algorithm to refine pseudo labels and supervise better object detector progressively.
Scaling Novel Object Detection with Weakly Supervised Detection Transformers
A critical object detection task is finetuning an existing model to detect novel objects, but the standard workflow requires bounding box annotations which are time-consuming and expensive to collect.
SESS: Saliency Enhancing with Scaling and Sliding
High-quality saliency maps are essential in several machine learning application areas including explainable AI and weakly supervised object detection and segmentation.
Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes
Semi- and weakly-supervised learning have recently attracted considerable attention in the object detection literature since they can alleviate the cost of annotation needed to successfully train deep learning models.
SIOD: Single Instance Annotated Per Category Per Image for Object Detection
Object detection under imperfect data receives great attention recently.
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
For unsupervised saliency detection, we improve IoU for 4. 9%, 5. 2%, 12. 9% on ECSSD, DUTS, DUT-OMRON respectively compared to previous state of the art.