Search Results for author:
Found 17 papers, 12 papers with code
Weakly Supervised Semantic Segmentation for Driving Scenes
Notably, the proposed method achieves 51. 8\% mIoU on the Cityscapes test dataset, showcasing its potential as a strong WSSS baseline on driving scene datasets.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Small Objects Matters in Weakly-supervised Semantic Segmentation
Weakly-supervised semantic segmentation (WSSS) performs pixel-wise classification given only image-level labels for training.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproducts
We refer to the new paradigm of training models with annotation byproducts as learning using annotation byproducts (LUAB).
Neglected Free Lunch - Learning Image Classifiers Using Annotation Byproducts
We refer to the new paradigm of training models with annotation byproducts as learning using annotation byproducts (LUAB).
Weakly Supervised Semantic Segmentation using Out-of-Distribution Data
However, training on class labels only, classifiers suffer from the spurious correlation between foreground and background cues (e. g. train and rail), fundamentally bounding the performance of WSSS.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Normalization Matters in Weakly Supervised Object Localization
Weakly-supervised object localization (WSOL) enables finding an object using a dataset without any localization information.
Keep CALM and Improve Visual Feature Attribution
The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks.
Rethinking Spatial Dimensions of Vision Transformers
We empirically show that such a spatial dimension reduction is beneficial to a transformer architecture as well, and propose a novel Pooling-based Vision Transformer (PiT) upon the original ViT model.
Ranked #333 on
Image Classification
on ImageNet
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
However, they have not fixed the training set, presumably because of a formidable annotation cost.
Ranked #20 on
Image Classification
on OmniBenchmark
Contrastive Attention Maps for Self-Supervised Co-Localization
The goal of unsupervised co-localization is to locate the object in a scene under the assumptions that 1) the dataset consists of only one superclass, e. g., birds, and 2) there are no human-annotated labels in the dataset.
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods
Despite apparent human-level performances of deep neural networks (DNN), they behave fundamentally differently from humans.
Evaluating Weakly Supervised Object Localization Methods Right
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
Attention-based Dropout Layer for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) techniques learn the object location only using image-level labels, without location annotations.
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Generative Adversarial Networks for Unsupervised Object Co-localization
Our important finding is that high image diversity of GAN, which is a main goal in GAN research, is ironically disadvantageous for object localization, because such discriminators focus not only on the target object, but also on the various objects, such as background objects.
Improved Techniques For Weakly-Supervised Object Localization
To this end, we employ an effective data augmentation for improving the accuracy of the object localization.
