  Transformer, which originates from machine translation, is particularly
powerful at modeling long-range dependencies. Currently, the transformer is
making revolutionary progress in various vision tasks, leading to significant
performance improvements compared with the convolutional neural network (CNN)
based frameworks. In this paper, we conduct extensive research on exploiting
the contributions of transformers for accurate and reliable salient object
detection. For the former, we apply transformer to a deterministic model, and
explain that the effective structure modeling and global context modeling
abilities lead to its superior performance compared with the CNN based
frameworks. For the latter, 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. To estimate the reliability
degree of both CNN- and transformer-based frameworks, we further present a
latent variable model, namely inferential generative adversarial network
(iGAN), based on the generative adversarial network (GAN). The stochastic
attribute of the latent variable makes it convenient to estimate the predictive
uncertainty, serving as an auxiliary output to evaluate the reliability of
model prediction. Different from the conventional GAN, which defines the
distribution of the latent variable as fixed standard normal distribution
$\mathcal{N}(0,\mathbf{I})$, the proposed iGAN infers the latent variable by
gradient-based Markov Chain Monte Carlo (MCMC), namely Langevin dynamics,
leading to an input-dependent latent variable model. We apply our proposed 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.
