Learning Hierarchical Features for Scene Labeling
Countries where authors are citing Learning Hierarchical Features for Scene Labeling
This map shows the geographic impact of Learning Hierarchical Features for Scene Labeling. 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 Learning Hierarchical Features for Scene Labeling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Learning Hierarchical Features for Scene Labeling more than expected).
Fields of papers citing Learning Hierarchical Features for Scene Labeling
This network shows the impact of Learning Hierarchical Features for Scene Labeling. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Learning Hierarchical Features for Scene Labeling.
About Learning Hierarchical Features for Scene Labeling
This paper, published in 2012, received 1.8k indexed citations . Written by Clément Farabet, Camille Couprie, Laurent Najman and Yann LeCun covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (1.1k citations), Artificial Intelligence (481 citations) and Media Technology (224 citations). Published in IEEE Transactions on Pattern Analysis and Machine Intelligence.
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/tpami.2012.231.