Generative Transformer for Accurate and Reliable Salient Object Detection

In this paper, we conduct extensive research on exploring the contribution of transformers to salient object detection, achieving both accurate and reliable saliency predictions. We first investigate transformers for accurate salient object detection with deterministic neural networks, and explain that the effective structure modeling and global context modeling abilities lead to its superior performance compared with the CNN based frameworks. Then, we design stochastic networks to evaluate the transformers' ability in reliable salient object detection. We observe that both CNN and transformer based frameworks suffer greatly from the over-confidence issue, where the models tend to generate wrong predictions with high confidence, leading to over-confident predictions or a poorly-calibrated model. To estimate the calibration degree of both CNN- and transformer-based frameworks for reliable saliency prediction, we introduce generative adversarial network (GAN) based models to identify the over-confident regions by sampling from the latent space. Specifically, we present the inferential generative adversarial network (iGAN). Different from the conventional GAN based framework, which defines the distribution of the latent variable as fixed standard normal distribution N(0,1), the proposed "iGAN" infers the latent variable by gradient-based Markov Chain Monte Carlo (MCMC), namely Langevin dynamics. We apply the proposed inferential generative adversarial network (iGAN) to both fully and weakly supervised salient object detection, and explain that iGAN within the transformer framework leads to both accurate and reliable salient object detection. The source code and experimental results are publicly available via our project page:

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.