Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence

Sparse labels have been attracting much attention in recent years. However, the performance gap between weakly supervised and fully supervised salient object detection methods is huge, and most previous weakly supervised works adopt complex training methods with many bells and whistles. In this work, we propose a one-round end-to-end training approach for weakly supervised salient object detection via scribble annotations without pre/post-processing operations or extra supervision data. Since scribble labels fail to offer detailed salient regions, we propose a local coherence loss to propagate the labels to unlabeled regions based on image features and pixel distance, so as to predict integral salient regions with complete object structures. We design a saliency structure consistency loss as self-consistent mechanism to ensure consistent saliency maps are predicted with different scales of the same image as input, which could be viewed as a regularization technique to enhance the model generalization ability. Additionally, we design an aggregation module (AGGM) to better integrate high-level features, low-level features and global context information for the decoder to aggregate various information. Extensive experiments show that our method achieves a new state-of-the-art performance on six benchmarks (e.g. for the ECSSD dataset: F_\beta = 0.8995, E_\xi = 0.9079 and MAE = 0.0489$), with an average gain of 4.60\% for F-measure, 2.05\% for E-measure and 1.88\% for MAE over the previous best method on this task. Source code is available at http://github.com/siyueyu/SCWSSOD.Comment: Accepted by AAAI202

Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation

Deep learning techniques have been widely used in autonomous driving systems for the semantic understanding of urban scenes. However, they need a huge amount of labeled data for training, which is difficult and expensive to acquire. A recently proposed workaround is to train deep networks using synthetic data, but the domain shift between real world and synthetic representations limits the performance. In this work, a novel Unsupervised Domain Adaptation (UDA) strategy is introduced to solve this issue. The proposed learning strategy is driven by three components: a standard supervised learning loss on labeled synthetic data; an adversarial learning module that exploits both labeled synthetic data and unlabeled real data; finally, a self-teaching strategy applied to unlabeled data. The last component exploits a region growing framework guided by the segmentation confidence. Furthermore, we weighted this component on the basis of the class frequencies to enhance the performance on less common classes. Experimental results prove the effectiveness of the proposed strategy in adapting a segmentation network trained on synthetic datasets, like GTA5 and SYNTHIA, to real world datasets like Cityscapes and Mapillary.Comment: Accepted at IEEE Transactions on Intelligent Vehicles (T-IV) 10 pages, 2 figures, 7 table

Adversarial Learning Strategies for Semantic Segmentation

Semantic image segmentation is a computer vision task in which we label specific regions of an image according to their semantic content. This task is of essential importance for a wide range of applications like robotics, autonomous driving, medicine and image editing. Although many datasets have been built for this task, they are typically generic while a specic problem could require to focus more on the data related to it. One of the biggest problems is represented by the difficulty of gathering large datasets. This is caused by the intrinsic complexity and cost of producing fine detailed ground truth for the interested data, as it consists in manually classifying each pixel of the images. In this work we tried to mitigate this problem developing and testing new techniques to perform semi-supervised training and domain adaptation with unlabeled data. Our framework started from some works, presented in the literature, which exploit an adversarial learning framework in order to train a segmentation network using both supervised and unsupervised data. Finally, we developed some extensions that further improve the performances of the unsupervised training process