  Humans perform co-saliency detection by first summarizing the consensus
knowledge in the whole group and then searching corresponding objects in each
image. Previous methods usually lack robustness, scalability, or stability for
the first process and simply fuse consensus features with image features for
the second process. In this paper, we propose a novel consensus-aware dynamic
convolution model to explicitly and effectively perform the "summarize and
search" process. To summarize consensus image features, we first summarize
robust features for every single image using an effective pooling method and
then aggregate cross-image consensus cues via the self-attention mechanism. By
doing this, our model meets the scalability and stability requirements. Next,
we generate dynamic kernels from consensus features to encode the summarized
consensus knowledge. Two kinds of kernels are generated in a supplementary way
to summarize fine-grained image-specific consensus object cues and the coarse
group-wise common knowledge, respectively. Then, we can effectively perform
object searching by employing dynamic convolution at multiple scales. Besides,
a novel and effective data synthesis method is also proposed to train our
network. Experimental results on four benchmark datasets verify the
effectiveness of our proposed method. Our code and saliency maps are available
at \url{https://github.com/nnizhang/CADC}.
