Various studies have been conducted on instance segmentation and made great strides over the past few years. Most recently, instance-specific mask generation via dynamic kernel predictions has shown the significant performance improvement even without bounding boxes as well as anchors. However, this scheme still does not fully consider dynamic properties since the size of the receptive field is not enough to cover the spatially-meaningful range due to memory limitations. Furthermore, the single-fused feature often fails to grasp complicated boundaries for objects of different sizes. In this article, we propose the dynamic residual filtering method with the Laplacian pyramid, which separately restores the global layout and local boundaries of instance masks. Specifically, we firstly apply the Laplacian pyramid-based decomposition scheme to features encoded from the backbone and subsequently restore sub-band mask residuals from coarse to fine pyramid levels. To do this, we design spatially-aware convolution filters to progressively capture the residual form of mask features at each level of the Laplacian pyramid while holding deformable receptive fields with dynamic offset information. This is fairly desirable since global and local properties of mask features can be accurately restored with keeping the spatial flexibility through the invertible process of the Laplacian reconstruction. Experimental results on the COCO dataset demonstrate that our proposed method achieves the state-of-the-art performance, i.e., 42.7% AP. The code and model are publicly available at:https://github.com/tjqansthd/LapMask.

With a great success of the generative model via deep neural networks, monocular depth estimation has been actively studied by exploiting various encoder-decoder architectures. However, the decoding process in most previous methods, which repeats simple up-sampling operations, probably fails to fully utilize underlying properties of well-encoded features for monocular depth estimation. To resolve this problem, we propose a simple but effective scheme by incorporating the Laplacian pyramid into the decoder architecture. Specifically, encoded features are fed into different streams for decoding depth residuals, which are defined by decomposition of the Laplacian pyramid, and corresponding outputs are progressively combined to reconstruct the final depth map from coarse to fine scales. This is fairly desirable to precisely estimate the depth boundary as well as the global layout. We also propose to apply weight standardization to pre-activation convolution blocks of the decoder architecture, which gives a great help to improve the flow of gradients and thus makes optimization easier. Experimental results on benchmark datasets constructed under various indoor and outdoor environments demonstrate that the proposed method is effective for monocular depth estimation compared to state-of-the-art models. The code and model are publicly available at: |https://github.com/tjqansthd/LapDepth-release|.