Search Results for author: Peng-Tao Jiang

Found 6 papers, 5 papers with code

LayerCAM: Exploring Hierarchical Class Activation Maps for Localization

2 code implementations IEEE 2021 Peng-Tao Jiang, Chang-Bin Zhang, Qibin Hou, Ming-Ming Cheng, Yunchao Wei

To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation.

Semantic Segmentation Weakly-Supervised Object Localization

Delving Deep into Label Smoothing

2 code implementations25 Nov 2020 Chang-Bin Zhang, Peng-Tao Jiang, Qibin Hou, Yunchao Wei, Qi Han, Zhen Li, Ming-Ming Cheng

Experiments demonstrate that based on the same classification models, the proposed approach can effectively improve the classification performance on CIFAR-100, ImageNet, and fine-grained datasets.

Classification General Classification

Integral Object Mining via Online Attention Accumulation

2 code implementations ICCV 2019 Peng-Tao Jiang, Qibin Hou, Yang Cao, Ming-Ming Cheng, Yunchao Wei, Hong-Kai Xiong

In order to accumulate the discovered different object parts, we propose an online attention accumulation (OAA) strategy which maintains a cumulative attention map for each target category in each training image so that the integral object regions can be gradually promoted as the training goes.

General Classification Weakly supervised segmentation +1

Self-Erasing Network for Integral Object Attention

no code implementations NeurIPS 2018 Qibin Hou, Peng-Tao Jiang, Yunchao Wei, Ming-Ming Cheng

To test the quality of the generated attention maps, we employ the mined object regions as heuristic cues for learning semantic segmentation models.

Semantic Segmentation

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