Weakly supervised object localization and detection: A survey
As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for developing new …
supervised object localization and detection plays an important role for developing new …
Regional semantic contrast and aggregation for weakly supervised semantic segmentation
Learning semantic segmentation from weakly-labeled (eg, image tags only) data is
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …
C2am: Contrastive learning of class-agnostic activation map for weakly supervised object localization and semantic segmentation
While class activation map (CAM) generated by image classification network has been
widely used for weakly supervised object localization (WSOL) and semantic segmentation …
widely used for weakly supervised object localization (WSOL) and semantic segmentation …
Pseudoseg: Designing pseudo labels for semantic segmentation
Recent advances in semi-supervised learning (SSL) demonstrate that a combination of
consistency regularization and pseudo-labeling can effectively improve image classification …
consistency regularization and pseudo-labeling can effectively improve image classification …
Ts-cam: Token semantic coupled attention map for weakly supervised object localization
Weakly supervised object localization (WSOL) is a challenging problem when given image
category labels but requires to learn object localization models. Optimizing a convolutional …
category labels but requires to learn object localization models. Optimizing a convolutional …
Group-wise learning for weakly supervised semantic segmentation
Acquiring sufficient ground-truth supervision to train deep visual models has been a
bottleneck over the years due to the data-hungry nature of deep learning. This is …
bottleneck over the years due to the data-hungry nature of deep learning. This is …
Shallow feature matters for weakly supervised object localization
Weakly supervised object localization (WSOL) aims to localize objects by only utilizing
image-level labels. Class activation maps (CAMs) are the commonly used features to …
image-level labels. Class activation maps (CAMs) are the commonly used features to …
Unveiling the potential of structure preserving for weakly supervised object localization
Weakly supervised object localization (WSOL) remains an open problem due to the
deficiency of finding object extent information using a classification network. While prior …
deficiency of finding object extent information using a classification network. While prior …
Online refinement of low-level feature based activation map for weakly supervised object localization
We present a two-stage learning framework for weakly supervised object localization
(WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation …
(WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation …
Foreground activation maps for weakly supervised object localization
Weakly supervised object localization (WSOL) aims to localize objects with only image-level
labels, which has better scalability and practicability than fully supervised methods in the …
labels, which has better scalability and practicability than fully supervised methods in the …