Temporal Convolutional Networks for Action Segmentation and Detection
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doi.org/10.1109/cvpr.2017.113 →Countries where authors are citing Temporal Convolutional Networks for Action Segmentation and Detection
This map shows the geographic impact of Temporal Convolutional Networks for Action Segmentation and Detection. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Temporal Convolutional Networks for Action Segmentation and Detection with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Temporal Convolutional Networks for Action Segmentation and Detection more than expected).
Fields of papers citing Temporal Convolutional Networks for Action Segmentation and Detection
This network shows the impact of Temporal Convolutional Networks for Action Segmentation and Detection. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Temporal Convolutional Networks for Action Segmentation and Detection.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/cvpr.2017.113.