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CCNet: Criss-Cross Attention for Semantic Segmentation
TLDR
This work proposes a Criss-Cross Network (CCNet) for obtaining contextual information in a more effective and efficient way and achieves the mIoU score of 81.4 and 45.22 on Cityscapes test set and ADE20K validation set, respectively, which are the new state-of-the-art results.
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach
TLDR
This work investigates a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems and proposes a new adversarial erasing approach for localizing and expanding object regions progressively.
Adversarial Complementary Learning for Weakly Supervised Object Localization
TLDR
This work mathematically proves that class localization maps can be obtained by directly selecting the class-specific feature maps of the last convolutional layer, which paves a simple way to identify object regions and presents a simple network architecture including two parallel-classifiers for object localization.
Self-Similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-Identification
TLDR
A Self-similarity Grouping (SSG) approach, which exploits the potential similarity of unlabeled samples to build multiple clusters from different views automatically, and introduces a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting.
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation
TLDR
It is found that varying dilation rates can effectively enlarge the receptive fields of convolutional kernels and more importantly transfer the surrounding discriminative information to non-discriminative object regions, promoting the emergence of these regions in the object localization maps.
Self-produced Guidance for Weakly-supervised Object Localization
TLDR
Self-produced Guidance (SPG) masks which separate the foreground i.e., the object of interest, from the background to provide the classification networks with spatial correlation information of pixels are proposed.
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
TLDR
This article proposes a simple yet effective similarity guidance network to tackle the one-shot (SG-One) segmentation problem, aiming at predicting the segmentation mask of a query image with the reference to one densely labeled support image of the same category.
Horizontal Pyramid Matching for Person Re-identification
TLDR
A simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be still identified even even some key parts are missing.
STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation
TLDR
A simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation, which demonstrates the superiority of the proposed STC framework compared with other state-of-the-arts frameworks.
Devil in the Details: Towards Accurate Single and Multiple Human Parsing
TLDR
This paper identifies several useful properties, including feature resolution, global context information and edge details, and performs rigorous analyses to reveal how to leverage them to benefit the human parsing task, resulting in a simple yet effective Context Embedding with Edge Perceiving (CE2P) framework for single human parsing.
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