137 citations · 234 across the 28 of their papers we have counts for
32 papers
SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation
Junliang Chen, Xiaodong Zhao, Minmin Liu +1
Recent mainstream weakly-supervised semantic segmentation (WSSS) approaches mainly relies on image-level classification learning, which has limited representation capacity. In this…
SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic Segmentation
Junliang Chen, Xiaodong Zhao, Cheng Luo +1
Recent mainstream weakly supervised semantic segmentation (WSSS) approaches are mainly based on Class Activation Map (CAM) generated by a CNN (Convolutional Neural Network) based i…
A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays
Haoqin Ji, Haozhe Liu, Yuexiang Li +7
Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed…
Robust Representation via Dynamic Feature Aggregation
Haozhe Liu, Haoqin Ji, Yuexiang Li +5
Deep convolutional neural network (CNN) based models are vulnerable to the adversarial attacks. One of the possible reasons is that the embedding space of CNN based model is sparse…
Scene Consistency Representation Learning for Video Scene Segmentation
Haoqian Wu, Keyu Chen, Yanan Luo +5
A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene…
Cross Language Image Matching for Weakly Supervised Semantic Segmentation
Jinheng Xie, Xianxu Hou, Kai Ye +1
It has been widely known that CAM (Class Activation Map) usually only activates discriminative object regions and falsely includes lots of object-related backgrounds. As only a fix…