{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1061"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1061","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Joint spatial and layer attention for convolutional networks","abstract":"In this work, we propose a novel approach that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., “what” feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., “where”) to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) step, both a CNN layer and localized spatial region within it are selected for further processing. We demonstrate the effectiveness of this approach on two computer vision tasks: (i) image-based six degree of freedom camera pose regression and (ii) indoor scene classification. Empirically, we show that combining the “what” and “where” aspects of attention improves network performance on both tasks. We evaluate our method on standard benchmarks for camera localization (Cambridge, 7-Scenes, and TUM-LSI) and for scene classification (MIT-67 Indoor Scenes). For camera localization our approach reduces the median error by 18.8% for position and 8.2% for orientation (averaged over all scenes), and for scene classification it improves the mean accuracy by 3.4% over previous methods.","abstract_html":"In this work, we propose a novel approach that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., “what” feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., “where”) to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) step, both a CNN layer and localized spatial region within it are selected for further processing. We demonstrate the effectiveness of this approach on two computer vision tasks: (i) image-based six degree of freedom camera pose regression and (ii) indoor scene classification. Empirically, we show that combining the “what” and “where” aspects of attention improves network performance on both tasks. We evaluate our method on standard benchmarks for camera localization (Cambridge, 7-Scenes, and TUM-LSI) and for scene classification (MIT-67 Indoor Scenes). For camera localization our approach reduces the median error by 18.8% for position and 8.2% for orientation (averaged over all scenes), and for scene classification it improves the mean accuracy by 3.4% over previous methods.","abstract_has_math":false,"creators":["Joseph, Tony"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Qureshi, Faisal","Derpanis, Kosta"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-05-01","date_published":"2019-05-01","updated_at":"2026-07-24T05:35:26Z","subjects":["Computational Attention","Convolutional Neural Networks","Reccurent Neural Networks","Neural Networks","Image-Based Camera Localization"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1061","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Qureshi, Faisal","Derpanis, Kosta"]},{"key":"dc:creator","label":"Author","values":["Joseph, Tony"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-07-17T15:31:58Z","2022-03-29T17:25:51Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-07-17T15:31:58Z","2022-03-29T17:25:51Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-05-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computational Attention","Convolutional Neural Networks","Reccurent Neural Networks","Neural Networks","Image-Based Camera Localization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1061"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this work, we propose a novel approach that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., “what” feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., “where”) to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) step, both a CNN layer and localized spatial region within it are selected for further processing. We demonstrate the effectiveness of this approach on two computer vision tasks: (i) image-based six degree of freedom camera pose regression and (ii) indoor scene classification. Empirically, we show that combining the “what” and “where” aspects of attention improves network performance on both tasks. We evaluate our method on standard benchmarks for camera localization (Cambridge, 7-Scenes, and TUM-LSI) and for scene classification (MIT-67 Indoor Scenes). For camera localization our approach reduces the median error by 18.8% for position and 8.2% for orientation (averaged over all scenes), and for scene classification it improves the mean accuracy by 3.4% over previous methods."]},{"key":"dc:title","label":"Title","values":["Joint spatial and layer attention for convolutional networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Qureshi, Faisal","Derpanis, Kosta"],"dc:creator":["Joseph, Tony"],"dc:date.accessioned":["2019-07-17T15:31:58Z","2022-03-29T17:25:51Z"],"dc:date.available":["2019-07-17T15:31:58Z","2022-03-29T17:25:51Z"],"dc:date.issued":["2019-05-01"],"dc:description.abstract":["In this work, we propose a novel approach that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., “what” feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., “where”) to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) step, both a CNN layer and localized spatial region within it are selected for further processing. We demonstrate the effectiveness of this approach on two computer vision tasks: (i) image-based six degree of freedom camera pose regression and (ii) indoor scene classification. Empirically, we show that combining the “what” and “where” aspects of attention improves network performance on both tasks. We evaluate our method on standard benchmarks for camera localization (Cambridge, 7-Scenes, and TUM-LSI) and for scene classification (MIT-67 Indoor Scenes). For camera localization our approach reduces the median error by 18.8% for position and 8.2% for orientation (averaged over all scenes), and for scene classification it improves the mean accuracy by 3.4% over previous methods."],"dc:identifier.uri":["https://hdl.handle.net/10155/1061"],"dc:language.iso":["en"],"dc:subject":["Computational Attention","Convolutional Neural Networks","Reccurent Neural Networks","Neural Networks","Image-Based Camera Localization"],"dc:title":["Joint spatial and layer attention for convolutional networks"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:26Z"}