Unifying Spatial-Temporal Homology and Local Surface Statistical Mapping for 6D Object Clustering


Unifying Spatial-Temporal Homology and Local Surface Statistical Mapping for 6D Object Clustering – We show that a simple spatial-temporal semantic segmentation method can outperform a state-of-the-art supervised segmentation approach. In this paper, we propose a novel semi-supervised segmentation approach to improve the performance of semantic segmentation, and we show that our approach can capture the semantic changes with a spatial-temporal similarity measure that can provide a better comparison between different approaches.

We evaluate two real-world problems: online scoring and offline scoring. One involves identifying the optimal scoring path for a given score set, while the other involves identifying the optimal scoring path for all scores. In this paper, we present algorithms for online scoring. Our algorithms are developed as an extension of the recent multi-label classification task. First, we learn the optimal score path through the combination of labels and scores. Second, we provide algorithmically rigorous evaluation results that show that the performance of the algorithms are comparable or better than the existing state-of-the-art algorithms. Experiments using both synthetic and real data show that our algorithms are efficient and robust to a significant loss in accuracy, especially when a novel scoring path is assigned to the scores.

Learning Structurally Shallow and Deep Features for Weakly Supervised Object Detection

Linear Convergence of Recurrent Neural Networks with Non-convex Loss Functions

Unifying Spatial-Temporal Homology and Local Surface Statistical Mapping for 6D Object Clustering

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  • Tensor-based transfer learning for image recognition

    Classification with Asymmetric Leader SelectionWe evaluate two real-world problems: online scoring and offline scoring. One involves identifying the optimal scoring path for a given score set, while the other involves identifying the optimal scoring path for all scores. In this paper, we present algorithms for online scoring. Our algorithms are developed as an extension of the recent multi-label classification task. First, we learn the optimal score path through the combination of labels and scores. Second, we provide algorithmically rigorous evaluation results that show that the performance of the algorithms are comparable or better than the existing state-of-the-art algorithms. Experiments using both synthetic and real data show that our algorithms are efficient and robust to a significant loss in accuracy, especially when a novel scoring path is assigned to the scores.


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