TESA: Tensor Element Self-Attention via Matricization

Representation learning is a fundamental part of modern computer vision, where abstract representations of data are encoded as tensors optimized to solve problems like image segmentation and inpainting. Recently, self-attention in the form of a Non-Local Block has emerged as a powerful technique to enrich features, by capturing complex interdependencies in feature tensors. However, standard selfattention approaches leverage only spatial relationships, drawing similarities between vectors and overlooking correlations between channels. In this paper, we introduce a new method, called Tensor Element Self-Attention (TESA) that generalizes such work to capture interdependencies along all dimensions of the tensor using matricization. An order R tensor produces R results, one for each dimension. The results are then fused to produce an enriched output which encapsulates similarity among tensor elements. Additionally, we analyze self-attention mathematically, providing new perspectives on how it adjusts the singular values of the input feature tensor. With these new insights, we present experimental results demonstrating how TESA can benefit diverse problems including classification and instance segmentation. By simply adding a TESA module to existing networks, we substantially improve competitive baselines and set new state-of-the-art results for image inpainting on CelebA and low light raw-to-rgb image translation on SID.