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name="continue" value="/citations?view_op=list_works&amp;hl=en&amp;hl=en&amp;user=UfbuDH8AAAAJ&amp;sortby=title"></form><div id="gsc_oci_title_wrapper"><div id="gsc_oci_title"><a class="gsc_oci_title_link" href="https://scholar.google.com/scholar?cluster=5332362754634500091&amp;hl=en&amp;oi=scholarr" data-clk="hl=en&amp;sa=T&amp;ei=37hCZ8CNJ6aIy9YPj4bQgAQ">From large-scale object classifiers to large-scale object detectors: An adaptation approach</a></div></div><div id="gsc_oci_table"><div class="gs_scl"><div class="gsc_oci_field">Authors</div><div class="gsc_oci_value">Judy Hoffman, Sergio Guadarrama, Eric Tzeng, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko, UMass Lowell CS</div></div><div class="gs_scl"><div class="gsc_oci_field">Publication date</div><div class="gsc_oci_value">2014</div></div><div class="gs_scl"><div class="gsc_oci_field">Journal</div><div class="gsc_oci_value">Proc. Adv. Neural Inf. Process. Syst</div></div><div class="gs_scl"><div class="gsc_oci_field">Pages</div><div class="gsc_oci_value">3536-3544</div></div><div class="gs_scl"><div class="gsc_oci_field">Description</div><div class="gsc_oci_value" id="gsc_oci_descr"><div class="gsh_small"><div class="gsh_csp">A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNN) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2 M+ labeled classification images. Unfortunately, only a small fraction of those labels are available for the detection task. It is much cheaper and easier to collect large quantities of image-level labels from search engines than it is to collect detection data and label it with precise bounding boxes. In this paper, we propose a Deep Detection Adaptation (DDA) algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors. Our method has the potential to enable detection for the tens of thousands of categories that lack bounding box annotations, yet have plenty of classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge demonstrates the efficacy of our approach.</div></div></div></div><div class="gs_scl"><div class="gsc_oci_field">Total citations</div><div class="gsc_oci_value"><div style="margin-bottom:1em"><a href="https://scholar.google.co.in/scholar?oi=bibs&amp;hl=en&amp;cites=5332362754634500091&amp;as_sdt=5">Cited by 4</a></div><div id="gsc_oci_graph_wrapper"><div id="gsc_oci_graph" style="width:294px;"><div id="gsc_oci_graph_x"></div><div id="gsc_oci_graph_bars"><span class="gsc_oci_g_t" style="left:0px">2015</span><span class="gsc_oci_g_t" style="left:33px">2016</span><span class="gsc_oci_g_t" style="left:66px">2017</span><span class="gsc_oci_g_t" style="left:99px">2018</span><span class="gsc_oci_g_t" style="left:132px">2019</span><span class="gsc_oci_g_t" style="left:165px">2020</span><span class="gsc_oci_g_t" style="left:198px">2021</span><span class="gsc_oci_g_t" style="left:231px">2022</span><span class="gsc_oci_g_t" style="left:264px">2023</span><a href="https://scholar.google.co.in/scholar?oi=bibs&amp;hl=en&amp;cites=5332362754634500091&amp;as_sdt=5&amp;as_ylo=2015&amp;as_yhi=2015" class="gsc_oci_g_a" style="left:5px;height:29px;top:28px;z-index:9"><span class="gsc_oci_g_al">1</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&amp;hl=en&amp;cites=5332362754634500091&amp;as_sdt=5&amp;as_ylo=2017&amp;as_yhi=2017" class="gsc_oci_g_a" style="left:71px;height:57px;top:0px;z-index:7"><span class="gsc_oci_g_al">2</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&amp;hl=en&amp;cites=5332362754634500091&amp;as_sdt=5&amp;as_ylo=2023&amp;as_yhi=2023" class="gsc_oci_g_a" style="left:269px;height:29px;top:28px;z-index:1"><span class="gsc_oci_g_al">1</span></a></div></div></div></div></div><div class="gs_scl"><div class="gsc_oci_field">Scholar articles</div><div class="gsc_oci_value"><div class="gsc_oci_merged_snippet"><div><a href="/scholar?oi=bibs&amp;cluster=5332362754634500091&amp;btnI=1&amp;hl=en">From large-scale object classifiers to large-scale object detectors: An adaptation approach</a></div><div>J Hoffman, S Guadarrama, E Tzeng, J Donahue… - Proc. Adv. Neural Inf. Process. Syst, 2014</div><div><a class="gsc_oms_link" href="https://scholar.google.co.in/scholar?oi=bibs&amp;hl=en&amp;cites=5332362754634500091&amp;as_sdt=5">Cited by 4</a> <a class="gsc_oms_link" href="https://scholar.google.co.in/scholar?oi=bibs&amp;hl=en&amp;q=related:-0eDW6xbAEoJ:scholar.google.com/">Related articles</a> </div></div></div></div></div></div></div></div><div id="gs_ftr_sp" role="presentation"></div><div id="gs_ftr" class="gs_md_rmb" role="contentinfo"><div id="gs_ftr_rt"><a href="//www.google.co.in/intl/en/policies/privacy/">Privacy</a><a href="//www.google.co.in/intl/en/policies/terms/">Terms</a><a href="javascript:void(0)" ontouchstart="gs_evt_dsp(event)" role="button" aria-controls="gs_ftr_mnu" aria-haspopup="true" class="gs_press gs_md_tb">Help</a></div><div id="gs_ftr_mnu" class="gs_md_d gs_md_ds gs_ttzi gs_md_ulr" role="menu" tabindex="-1"><a role="menuitem" href="/intl/en/scholar/about.html" tabindex="-1" class="gs_md_li">About Scholar</a><a role="menuitem" href="//support.google.com/websearch?p=scholar_dsa&amp;hl=en" tabindex="-1" class="gs_md_li">Search help</a></div></div></div></body></html>

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