Energy-Based Generative Cooperative Saliency Prediction

Conventional saliency prediction models typically learn a deterministic mapping from images to the corresponding ground truth saliency maps. In this paper, we study the saliency prediction problem from the perspective of generative models by learning a conditional probability distribution over saliency maps given an image, and treating the prediction as a sampling process. Specifically, we propose a generative cooperative saliency prediction framework based on the generative cooperative networks, where a conditional latent variable model and a conditional energy-based model are jointly trained to predict saliency in a cooperative manner. We call our model the SalCoopNets. The latent variable model serves as a fast but coarse predictor to efficiently produce an initial prediction, which is then refined by the iterative Langevin revision of the energy-based model that serves as a fine predictor. Such a coarse-to-fine cooperative saliency prediction strategy offers the best of both worlds. Moreover, we generalize our framework to the scenario of weakly supervised saliency prediction, where saliency annotation of training images is partially observed, by proposing a cooperative learning while recovering strategy. Lastly, we show that the learned energy function can serve as a refinement module that can refine the results of other pre-trained saliency prediction models. Experimental results show that our generative model can achieve state-of-the-art performance. Our code is publicly available at: \url{https://github.com/JingZhang617/SalCoopNets}

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

Few-Cost Salient Object Detection with Adversarial-Paced Learning

Detecting and segmenting salient objects from given image scenes has received great attention in recent years. A fundamental challenge in training the existing deep saliency detection models is the requirement of large amounts of annotated data. While gathering large quantities of training data becomes cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. To address this problem, this paper proposes to learn the effective salient object detection model based on the manual annotation on a few training images only, thus dramatically alleviating human labor in training models. To this end, we name this task as the few-cost salient object detection and propose an adversarial-paced learning (APL)-based framework to facilitate the few-cost learning scenario. Essentially, APL is derived from the self-paced learning (SPL) regime but it infers the robust learning pace through the data-driven adversarial learning mechanism rather than the heuristic design of the learning regularizer. Comprehensive experiments on four widely-used benchmark datasets demonstrate that the proposed method can effectively approach to the existing supervised deep salient object detection models with only 1k human-annotated training images. The project page is available at https://github.com/hb-stone/FC-SOD