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The pixel-wise dense prediction tasks based on weakly supervisions currentlyuse Class Attention Maps (CAM) to generate pseudo masks as ground-truth.However, the existing methods typically depend on the painstaking trainingmodules, which may bring in grinding computational overhead and complextraining procedures. In this work, the semantic structure aware inference (SSA)is proposed to explore the semantic structure information hidden in differentstages of the CNN-based network to generate high-quality CAM in the modelinference. Specifically, the semantic structure modeling module (SSM) is firstproposed to generate the class-agnostic semantic correlation representation,where each item denotes the affinity degree between one category of objects andall the others. Then the structured feature representation is explored topolish an immature CAM via the dot product operation. Finally, the polishedCAMs from different backbone stages are fused as the output. The proposedmethod has the advantage of no parameters and does not need to be trained.Therefore, it can be applied to a wide range of weakly-supervised pixel-wisedense prediction tasks. Experimental results on both weakly-supervised objectlocalization and weakly-supervised semantic segmentation tasks demonstrate theeffectiveness of the proposed method, which achieves the new state-of-the-artresults on these two tasks.

SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost