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 …
Reducing information bottleneck for weakly supervised semantic segmentation
Weakly supervised semantic segmentation produces pixel-level localization from class
labels; however, a classifier trained on such labels is likely to focus on a small discriminative …
labels; however, a classifier trained on such labels is likely to focus on a small discriminative …
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 …
Generative prompt model for weakly supervised object localization
Weakly supervised object localization (WSOL) remains challenging when learning object
localization models from image category labels. Conventional methods that discriminatively …
localization models from image category labels. Conventional methods that discriminatively …
Diswot: Student architecture search for distillation without training
Abstract Knowledge distillation (KD) is an effective training strategy to improve the
lightweight student models under the guidance of cumbersome teachers. However, the large …
lightweight student models under the guidance of cumbersome teachers. However, the large …
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 …
Learning multi-modal class-specific tokens for weakly supervised dense object localization
Weakly supervised dense object localization (WSDOL) relies generally on Class Activation
Mapping (CAM), which exploits the correlation between the class weights of the image …
Mapping (CAM), which exploits the correlation between the class weights of the image …
Max pooling with vision transformers reconciles class and shape in weakly supervised semantic segmentation
Abstract Weakly Supervised Semantic Segmentation (WSSS) research has explored many
directions to improve the typical pipeline CNN plus class activation maps (CAM) plus …
directions to improve the typical pipeline CNN plus class activation maps (CAM) plus …
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 …