Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse depth data with optional dense RGB, and accomplish depth completion and semantic segmentation changing only the last layer. Our proposal efficiently learns sparse features without the need of an additional validity mask. We show how to ensure network robustness to varying input sparsities. Our method even works with densities as low as 0.8 (8 layer lidar), and outperforms all published state-of-the-art on the Kitti depth completion benchmark.
Other alternatives either act directly on the data manifold @cite_19 to avoid the extension of the valid domain or apply order-invariant operations @cite_1 .
@cite_19 @cite_1
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After a discussion on definability of invariant subdivision rules we discuss rules for sequential data living in Riemannian manifolds and in symmetric spaces, having in mind the space of positive definite matrices as a major example. We show that subdivision rules defined with intrinsic means in Cartan-Hadamard manifolds converge for all input data, which is a much stronger result than those usually available for manifold subdivision rules. We also show weaker convergence results which are true in general but apply only to dense enough input data. Finally we discuss C 1 and C 2 smoothness of limit curves. Verification using static analysis often hinges on precise numeric invariants. Numeric domains of infinite height can infer these invariants, but require widening narrowing which complicates the fixpoint computation and is often too imprecise. As a consequence, several strategies have been proposed to prevent a precision loss during widening or to narrow in a smarter way. Most of these strategies are difficult to retrofit into an existing analysis as they either require a pre-analysis, an on-the-fly modification of the CFG, or modifications to the fixpoint algorithm. We propose to encode widening and its various refinements from the literature as cofibered abstract domains that wrap standard numeric domains, thereby providing a modular way to add numeric analysis to any static analysis, that is, without modifying the fixpoint engine. Since these domains cannot make any assumptions about the structure of the program, our approach is suitable to the analysis of executables, where the (potentially irreducible) CFG is re-constructed on-the-fly. Moreover, our domain-based approach not only mirrors the precision of more intrusive approaches in the literature but also requires fewer iterations to find a fixpoint of loops than many heuristics that merely aim for precision. Abstract Performing effective image retrieval tasks, capable of exploiting the underlying structure of datasets still constitutes a challenge research scenario. This paper proposes a novel manifold learning approach that exploits the intrinsic dataset geometry for improving the effectiveness of image retrieval tasks. The underlying dataset manifold is modeled and analyzed in terms of a Reciprocal kNN Graph and its Connected Components. The method computes the new retrieval results on an unsupervised way, without the need of any user intervention. A large experimental evaluation was conducted, considering different image retrieval tasks, various datasets and features. The proposed method yields better effectiveness results than various methods recently proposed, achieving effectiveness gains up to +40.75 . Extracting the domain-invariant features provides an important intuition for unsupervised domain adaptation. Due to the unavailable target labels, it is difficult to guarantee that the learned domain-invariant features are good for target instances classification. In this paper, we extend the classic geodesic flow kernel method by leveraging the pseudo labels during the training process to learn a discriminative geodesic flow kernel for unsupervised domain adaptation. Specifically, the proposed method alternately discovers the pseudo target labels and builds the geodesic flow from a discriminative source subspace to another ‘discriminative’ target subspace. More specially, the pseudo target labels are inferred via the learned kernel based on an easy yet effective label propagation strategy. Hence, the proposed method not only holds the property of domain-invariance, but also maximizes the consistency between pseudo label structure and data structure. Experimental results illustrate that the proposed method outperforms the state-of-the-art unsupervised domain adaptation methods for object recognition and sentiment analysis.