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Experimental results on CoNLL benchmark dataset show that our proposed DSMM significantly outperforms existing baseline models for EL task.
Results on the dataset show that neural models can achieve better performance compared to state-of-the-art discrete models.
Experimental results on an actual public bus transportation dataset show that this method outperforms some existing methods.
Evaluations on the Princeton Segmentation Benchmark dataset show that our framework significantly outperforms other state-of-the-art methods.
a Jumps.mp4 and (b) Statue of Liberty.mp4 in the SumMe dataset show consistent camerawork such as zoom and pan.
Experiments on a public dataset show that this method provides significant average quality improvement for streaming video applications.
Experimental results carried out on a real platform in our laboratory and by using the Victoria park dataset show the performance of the approach.
Experiments performed on a large publicly available benchmarking dataset show that our approach performs better with respect to other state-of-the art methods.
Experimental results on the LAG dataset show that our method is able to outperform the face verification solutions in the state of the art considered.
Experimental results on the ICDAR 2011 competition dataset show that the proposed approach outperforms state-of-the-art methods both in recall and precision.
Experiments performed on angiograms of a dataset show that the proposed algorithm has a Dice score of 81.51 and an accuracy of 97.93.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com