var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://leelabcnbc.github.io/lab-wiki-builder/demo_ref/computer_vision/caption_generation/bib.bib\",\"jsonp\":\"1\",\"host\":\"bibbase.org\",\"ssl\":false},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Xu\"],\"firstnames\":[\"Kevin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ba\"],\"firstnames\":[\"Jimmy\",\"Lei\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kiros\"],\"firstnames\":[\"Ryan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cho\"],\"firstnames\":[\"Kyunghyun\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Courville\"],\"firstnames\":[\"Aaron\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Salakhutdinov\"],\"firstnames\":[\"Ruslan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zemel\"],\"firstnames\":[\"Richard\",\"S\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bengio\"],\"firstnames\":[\"Yoshua\"],\"suffixes\":[]}],\"title\":\"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention\",\"journal\":\"ArXiv e-prints\",\"year\":\"2015\",\"volume\":\"cs.LG\",\"month\":\"February\",\"annote\":\"Great application of \\\"attention\\\" to image captioning. Notice that where to attend is not supervised, just like the case in CTC. CS231n says that one problem with this work is the resolution of attention. Maybe can be addressed using per pixel hypercolumn features, after upsampling. pp. 4. Check CS231n for the notation, notice that I think here T should be $T_{D+m+n, 4n}$. Section 4.1 Hard attention. This version is learned by maximizing lower bound. In Eq. (11), s is a vector of all choices along time. and to learn this, we need sampling. in pp.5, there are some further refinements on optimization. Maybe these are just some tricks in MCMC. anyway. Section 4.2 soft attention. pp. 6 most of left column is about connection between soft and hard versions. Ignored. Section 4.2.1 Doubly Stochastic Attention. No idea why across time, the alpha should sum to 1 for each location. See <https://github.com/kelvinxu/arctic-captions/issues/38> and <https://www.reddit.com/r/MachineLearning/comments/2vjdcw/show_attend_and_tell_neural_image_caption/cok4yc9/>. Maybe it just worked, whatsoever. In addition, that β stuff might be some hack that emprically helps. \",\"keywords\":\"deep learning, recurrent\",\"read\":\"Yes\",\"rating\":\"5\",\"date-added\":\"2017-02-21T16:11:00GMT\",\"date-modified\":\"2017-02-23T19:57:09GMT\",\"url\":\"http://arxiv.org/abs/1502.03044\",\"local-url\":\"file://localhost/Users/yimengzh/Documents/Papers3_revised/Library.papers3/Articles/2015/Xu/arXiv%202015%20Xu.pdf\",\"file\":\"arXiv 2015 Xu.pdf:/Users/yimengzh/Documents/Papers3_revised/Library.papers3/Articles/2015/Xu/arXiv 2015 Xu.pdf:application/pdf\",\"uri\":\"˘rlpapers3://publication/uuid/881D0A60-2E59-477D-814F-5E8908F34D89\",\"bibtex\":\"@article{Xu:2015ut,\\nauthor = {Xu, Kevin and Ba, Jimmy Lei and Kiros, Ryan and Cho, Kyunghyun and Courville, Aaron and Salakhutdinov, Ruslan and Zemel, Richard S and Bengio, Yoshua},\\ntitle = {{Show, Attend and Tell: Neural Image Caption Generation with Visual Attention}},\\njournal = {ArXiv e-prints},\\nyear = {2015},\\nvolume = {cs.LG},\\nmonth = feb,\\nannote = {Great application of \\\"attention\\\" to image captioning.\\n\\nNotice that where to attend is not supervised, just like the case in CTC.\\n\\nCS231n says that one problem with this work is the resolution of attention. Maybe can be addressed using per pixel hypercolumn features, after upsampling.\\n\\npp. 4.\\n\\nCheck CS231n for the notation, notice that I think here T should be $T_{D+m+n, 4n}$.\\n\\nSection 4.1 Hard attention.\\n\\nThis version is learned by maximizing lower bound. In Eq. (11), s is a vector of all choices along time. and to learn this, we need sampling.\\n\\nin pp.5, there are some further refinements on optimization. Maybe these are just some tricks in MCMC. anyway.\\n\\nSection 4.2 soft attention.\\n\\npp. 6\\n\\nmost of left column is about connection between soft and hard versions. Ignored.\\n\\n\\nSection 4.2.1 Doubly Stochastic Attention.\\n\\nNo idea why across time, the alpha should sum to 1 for each location. See <https://github.com/kelvinxu/arctic-captions/issues/38> and <https://www.reddit.com/r/MachineLearning/comments/2vjdcw/show_attend_and_tell_neural_image_caption/cok4yc9/>. Maybe it just worked, whatsoever.\\n\\nIn addition, that \\\\beta stuff might be some hack that emprically helps.\\n\\n},\\nkeywords = {deep learning, recurrent},\\nread = {Yes},\\nrating = {5},\\ndate-added = {2017-02-21T16:11:00GMT},\\ndate-modified = {2017-02-23T19:57:09GMT},\\nurl = {http://arxiv.org/abs/1502.03044},\\nlocal-url = {file://localhost/Users/yimengzh/Documents/Papers3_revised/Library.papers3/Articles/2015/Xu/arXiv%202015%20Xu.pdf},\\nfile = {{arXiv 2015 Xu.pdf:/Users/yimengzh/Documents/Papers3_revised/Library.papers3/Articles/2015/Xu/arXiv 2015 Xu.pdf:application/pdf}},\\nuri = {\\\\url{papers3://publication/uuid/881D0A60-2E59-477D-814F-5E8908F34D89}}\\n}\\n\",\"author_short\":[\"Xu, K.\",\"Ba, J. L.\",\"Kiros, R.\",\"Cho, K.\",\"Courville, A.\",\"Salakhutdinov, R.\",\"Zemel, R. 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L.; Kiros, R.; Cho, K.; Courville, A.; Salakhutdinov, R.; Zemel, R. S;  and Bengio, Y.\n</span>\n\n  \n\n</span>\n\n  <i>ArXiv e-prints</i>, cs.LG. February 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/1502.03044\"\n     onclick=\"log_download('xu-ba-kiros-cho-courville-salakhutdinov-zemel-bengio-showattendandtellneuralimagecaptiongenerationwithvisualattention-2015', 'http://arxiv.org/abs/1502.03044', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention [link]\"\n       title=\"Paper\"\n       style=\"max-height: 1em;\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/xu-ba-kiros-cho-courville-salakhutdinov-zemel-bengio-showattendandtellneuralimagecaptiongenerationwithvisualattention-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('xu-ba-kiros-cho-courville-salakhutdinov-zemel-bengio-showattendandtellneuralimagecaptiongenerationwithvisualattention-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Xu:2015ut,\nauthor = {Xu, Kevin and Ba, Jimmy Lei and Kiros, Ryan and Cho, Kyunghyun and Courville, Aaron and Salakhutdinov, Ruslan and Zemel, Richard S and Bengio, Yoshua},\ntitle = {{Show, Attend and Tell: Neural Image Caption Generation with Visual Attention}},\njournal = {ArXiv e-prints},\nyear = {2015},\nvolume = {cs.LG},\nmonth = feb,\nannote = {Great application of &quot;attention&quot; to image captioning.\n\nNotice that where to attend is not supervised, just like the case in CTC.\n\nCS231n says that one problem with this work is the resolution of attention. Maybe can be addressed using per pixel hypercolumn features, after upsampling.\n\npp. 4.\n\nCheck CS231n for the notation, notice that I think here T should be $T_{D+m+n, 4n}$.\n\nSection 4.1 Hard attention.\n\nThis version is learned by maximizing lower bound. In Eq. (11), s is a vector of all choices along time. and to learn this, we need sampling.\n\nin pp.5, there are some further refinements on optimization. Maybe these are just some tricks in MCMC. anyway.\n\nSection 4.2 soft attention.\n\npp. 6\n\nmost of left column is about connection between soft and hard versions. Ignored.\n\n\nSection 4.2.1 Doubly Stochastic Attention.\n\nNo idea why across time, the alpha should sum to 1 for each location. See &lt;https://github.com/kelvinxu/arctic-captions/issues/38&gt; and &lt;https://www.reddit.com/r/MachineLearning/comments/2vjdcw/show_attend_and_tell_neural_image_caption/cok4yc9/&gt;. Maybe it just worked, whatsoever.\n\nIn addition, that \\beta stuff might be some hack that emprically helps.\n\n},\nkeywords = {deep learning, recurrent},\nread = {Yes},\nrating = {5},\ndate-added = {2017-02-21T16:11:00GMT},\ndate-modified = {2017-02-23T19:57:09GMT},\nurl = {http://arxiv.org/abs/1502.03044},\nlocal-url = {file://localhost/Users/yimengzh/Documents/Papers3_revised/Library.papers3/Articles/2015/Xu/arXiv%202015%20Xu.pdf},\nfile = {{arXiv 2015 Xu.pdf:/Users/yimengzh/Documents/Papers3_revised/Library.papers3/Articles/2015/Xu/arXiv 2015 Xu.pdf:application/pdf}},\nuri = {\\url{papers3://publication/uuid/881D0A60-2E59-477D-814F-5E8908F34D89}}\n}\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);