DCGC introduces a dual-attention convolution network and group-level contrastive learning to improve sparse tensor completion. The method captures complex cross-mode interactions and reduces vulnerability to data sparsity through self-supervised signals, outperforming state-of-the-art approaches on traffic and recommendation datasets.