Weakly-Supervised Object Localization
75 papers with code • 8 benchmarks • 3 datasets
Weakly supervised object localization (WSOL) learns to localize objects with only image-level labels, no object level labels (bonding boxes, etc.,) is needed. It is more attractive since image-level labels are much easier and cheaper to obtain.
Libraries
Use these libraries to find Weakly-Supervised Object Localization models and implementationsLatest papers
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization Perspective
The reason for the high-performance of large kernel CNNs in downstream tasks has been attributed to the large effective receptive field (ERF) produced by large size kernels, but this view has not been fully tested.
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization
Subsequently, these proposals are used as pseudo-labels to train our new transformer-based WSOL model designed to perform classification and localization tasks.
Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation
In addition, our method also achieves state-of-the-art weakly supervised semantic segmentation performance on the PASCAL VOC 2012 and MS COCO 2014 datasets.
FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object Localization
To the best of our knowledge, we are the first to address this task.
Generative Prompt Model for Weakly Supervised Object Localization
During training, GenPromp converts image category labels to learnable prompt embeddings which are fed to a generative model to conditionally recover the input image with noise and learn representative embeddings.
Open-World Weakly-Supervised Object Localization
To handle such data, we propose a novel paradigm of contrastive representation co-learning using both labeled and unlabeled data to generate a complete G-CAM (Generalized Class Activation Map) for object localization, without the requirement of bounding box annotation.
Spatial-Aware Token for Weakly Supervised Object Localization
Specifically, a spatial token is first introduced in the input space to aggregate representations for localization task.
Knowledge-guided Causal Intervention for Weakly-supervised Object Localization
Previous weakly-supervised object localization (WSOL) methods aim to expand activation map discriminative areas to cover the whole objects, yet neglect two inherent challenges when relying solely on image-level labels.
Adversarial Normalization: I Can Visualize Everything (ICE)
Vision transformers use [CLS] tokens to predict image classes.
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks.