Why is Real-World Visual Object Recognition Hard?
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{"title"=>"Why is real-world visual object recognition hard?", "type"=>"journal", "authors"=>[{"first_name"=>"Nicolas", "last_name"=>"Pinto", "scopus_author_id"=>"35273071400"}, {"first_name"=>"David D.", "last_name"=>"Cox", "scopus_author_id"=>"8923945700"}, {"first_name"=>"James J.", "last_name"=>"DiCarlo", "scopus_author_id"=>"7006387907"}], "year"=>2008, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"351230862", "sgr"=>"38949193299", "issn"=>"1553734X", "arxiv"=>"arXiv:1202.2745v1", "pmid"=>"18225950", "scopus"=>"2-s2.0-38949193299", "doi"=>"10.1371/journal.pcbi.0040027", "isbn"=>"1553-7358"}, "id"=>"9a6a1a98-b533-3062-a7ba-6b7289844a69", "abstract"=>"Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, \"natural\" images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled \"natural\" images in guiding that progress. In particular, we show that a simple V1-like model--a neuroscientist's \"null\" model, which should perform poorly at real-world visual object recognition tasks--outperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a \"simpler\" recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition--real-world image variation.", "link"=>"http://www.mendeley.com/research/realworld-visual-object-recognition-hard", "reader_count"=>590, "reader_count_by_academic_status"=>{"Unspecified"=>10, "Professor > Associate Professor"=>30, "Librarian"=>2, "Student > Doctoral Student"=>22, "Researcher"=>116, "Student > Ph. D. Student"=>188, "Student > Postgraduate"=>17, "Other"=>25, "Student > Master"=>106, "Student > Bachelor"=>41, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>28}, "reader_count_by_user_role"=>{"Unspecified"=>10, "Professor > Associate Professor"=>30, "Librarian"=>2, "Student > Doctoral Student"=>22, "Researcher"=>116, "Student > Ph. D. Student"=>188, "Student > Postgraduate"=>17, "Other"=>25, "Student > Master"=>106, "Student > Bachelor"=>41, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>28}, "reader_count_by_subject_area"=>{"Unspecified"=>20, "Agricultural and Biological Sciences"=>65, "Arts and Humanities"=>4, "Chemistry"=>2, "Computer Science"=>276, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>1, "Engineering"=>73, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Materials Science"=>1, "Mathematics"=>6, "Medicine and Dentistry"=>13, "Neuroscience"=>33, "Sports and Recreations"=>2, "Physics and Astronomy"=>12, "Psychology"=>66, "Social Sciences"=>9, "Linguistics"=>4}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>13}, "Social Sciences"=>{"Social Sciences"=>9}, "Sports and Recreations"=>{"Sports and Recreations"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>12}, "Psychology"=>{"Psychology"=>66}, "Mathematics"=>{"Mathematics"=>6}, "Unspecified"=>{"Unspecified"=>20}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>4}, "Engineering"=>{"Engineering"=>73}, "Chemistry"=>{"Chemistry"=>2}, "Neuroscience"=>{"Neuroscience"=>33}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>65}, "Computer Science"=>{"Computer Science"=>276}, "Linguistics"=>{"Linguistics"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"Colombia"=>3, "United States"=>40, "Japan"=>2, "United Kingdom"=>5, "Ghana"=>1, "Switzerland"=>6, "Portugal"=>1, "Spain"=>4, "Russia"=>1, "Canada"=>4, "Czech Republic"=>1, "Netherlands"=>1, "Korea (South)"=>1, "Iran"=>1, "China"=>5, "Poland"=>1, "Italy"=>4, "South Africa"=>1, "Slovakia"=>1, "France"=>8, "Australia"=>4, "Chile"=>2, "Germany"=>15}, "group_count"=>27}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/462461", "https://ndownloader.figshare.com/files/462498", "https://ndownloader.figshare.com/files/462520", "https://ndownloader.figshare.com/files/462545"], "description"=>"<div><p>Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, “natural” images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled “natural” images in guiding that progress. In particular, we show that a simple V1-like model—a neuroscientist's “null” model, which should perform poorly at real-world visual object recognition tasks—outperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a “simpler” recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition—real-world image variation.</p></div>", "links"=>[], "tags"=>["real-world"], "article_id"=>151152, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David D Cox", "James J DiCarlo"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.0040027.sg001", "https://dx.doi.org/10.1371/journal.pcbi.0040027.sg002", "https://dx.doi.org/10.1371/journal.pcbi.0040027.sg003", "https://dx.doi.org/10.1371/journal.pcbi.0040027.sg004"], "stats"=>{"downloads"=>11, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Why_is_Real_World_Visual_Object_Recognition_Hard_/151152", "title"=>"Why is Real-World Visual Object Recognition Hard?", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2008-01-25 00:19:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/938196"], "description"=>"<div><p>(A) Example images from the database and their category labels.</p><p>(B) Two example images from the “car” category.</p><p>(C) Reported performance of five state-of-the-art computational object recognition systems on this “natural” database are shown in gray (1 = Wang et al. 2006; 2 = Grauman and Darrell 2006; 3 = Mutch and Lowe 2006; 4 = Lazebnik et al. 2006; 5 = Zhang et al. 2006). In this panel, 15 training examples were used to train each system. Since chance performance on this 102-category task is less than 1%, performance values greater than ∼40% have been taken as substantial progress. The performance of the simple V1-like model is shown in black (+ is with “ad hoc” features; see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0040027#s4\" target=\"_blank\">Methods</a>). Although the V1-like model is extremely simple and lacks any explicit invariance-building mechanisms, it performs as well as, or better than, state-of-the-art object recognition systems on the “natural” databases (but see Varma and Ray 2007 for a recent hybrid approach, that pools the above methods to achieve higher performance).</p><p>(D) Same as (C) except that 30 training examples were used. The dashed lines indicates performance achieved using an untransformed grayscale pixel space representation and a linear SVM classifier (15 training examples: 16.1%, SD 0.4; 30 training examples: 17.3%, SD 0.8). Error bars (barely visible) represent the standard deviation of the mean performance of the V1-like model over ten random training and testing splits of the images. The authors of the state-of-the-art approaches do not consistently report this variation, but when they do they are in the same range (less than 1%). The V1-model also performed favorably with fewer training examples (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0040027#pcbi-0040027-sg004\" target=\"_blank\">Figure S4</a>).</p></div>", "links"=>[], "tags"=>["v1-like", "state-of-the-art", "systems", "biologically", "ostensibly"], "article_id"=>608643, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David D Cox", "James J DiCarlo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0040027.g001", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_a_Simple_V1_Like_Model_Relative_to_Current_Performance_of_State_of_the_Art_Artificial_Object_Recognition_Systems_Some_Biologically_Inspired_on_an_Ostensibly_8220_Natural_8221_Standard_Image_Database_Caltech101_/608643", "title"=>"Performance of a Simple V1-Like Model Relative to Current Performance of State-of-the-Art Artificial Object Recognition Systems (Some Biologically Inspired) on an Ostensibly “Natural” Standard Image Database (Caltech101)", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-01-25 02:24:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/938300"], "description"=>"<div><p>(A) We used 3-D models of cars and planes to generate image sets for performing a cars-versus-planes two-category test. By using 3-D models, we were able to parametrically control the amount of identity-preserving variation that the system was required to tolerate to perform the task (i.e., variation in each object's position, scale, pose). The 3-D models were rendered using ray-tracing software (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0040027#s4\" target=\"_blank\">Methods</a>), and were placed on either a white noise background (shown here), a scene background, or a phase scrambled background (these backgrounds are themselves another form of variation that a recognition system must tolerate; see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0040027#pcbi-0040027-sg001\" target=\"_blank\">Figure S1</a>).</p><p>(B) As the amount of variation was increased (<i>x</i>-axis), performance drops off, eventually reaching chance level (50%). Here, we used 100 training and 30 testing images for each object category. However, using substantially more exemplar images (1,530 training, 1,530 testing) yielded only mild performance gains (e.g., 2.7% for the fourth variation level using white noise background), indicating that the failure of this model is not due to under-training. Error bars represent the standard error of the mean computed over ten random splits of training and testing images (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0040027#s4\" target=\"_blank\">Methods</a>). This result highlights a fundamental problem in the current use of “natural” images to test object recognition models. By the logic of the “natural” Caltech101 test set, this task should be easy, because it has just two object categories, while the Caltech101 test should be hard (102 object categories). However, this V1-like model fails badly with this “easy” set, in spite of high performance on the Caltech101 test set (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0040027#pcbi-0040027-g001\" target=\"_blank\">Figure 1</a>).</p></div>", "links"=>[], "tags"=>["v1-like", "performed", "fails", "explicitly", "requires"], "article_id"=>608747, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David D Cox", "James J DiCarlo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0040027.g002", "stats"=>{"downloads"=>5, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Same_Simple_V1_Like_Model_That_Performed_Well_in_Figure_1_Is_Not_a_Good_Model_of_Object_Recognition_It_Fails_Badly_on_a_Simple_Problem_That_Explicitly_Requires_Tolerance_to_Image_Variation_/608747", "title"=>"The Same Simple V1-Like Model That Performed Well in Figure 1 Is Not a Good Model of Object Recognition—It Fails Badly on a “Simple” Problem That Explicitly Requires Tolerance to Image Variation", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-01-25 02:25:47"}

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  • {"unique-ip"=>"61", "full-text"=>"63", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"73", "full-text"=>"76", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"37", "full-text"=>"38", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"56", "full-text"=>"61", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"52", "full-text"=>"64", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}

Relative Metric

{"start_date"=>"2008-01-01T00:00:00Z", "end_date"=>"2008-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[324, 674, 863, 1015, 1146, 1259, 1360, 1452, 1540, 1621, 1704, 1776, 1843, 1905, 1968, 2029, 2097, 2162, 2233, 2289, 2352, 2412, 2476, 2538, 2601, 2655, 2714, 2777, 2841, 2894, 2946, 2998, 3047, 3103, 3151, 3201, 3258, 3317, 3369, 3424, 3471, 3521, 3583, 3625, 3687, 3743, 3788, 3838, 3896, 3953, 4015, 4075, 4148, 4201, 4275, 4347, 4420, 4486, 4535, 4592, 4660, 4731, 4792, 4845, 4901, 4971, 5027, 5089, 5148, 5200, 5261, 5318, 5374]}, {"subject_area"=>"/Biology and life sciences/Behavior", "average_usage"=>[355, 717, 919, 1114, 1250, 1341, 1418, 1485, 1548, 1629, 1727, 1782, 1846, 1921, 1991, 2068, 2161, 2237, 2318, 2392, 2456, 2519, 2575, 2625, 2681, 2734, 2790, 2859, 2920, 2976, 3024, 3047, 3093, 3156, 3193, 3234, 3256, 3306, 3362, 3405, 3446, 3497, 3553, 3664, 3730, 3797, 3856, 3919, 3967, 4018, 4099, 4185, 4258, 4352, 4405, 4456, 4521, 4589, 4634, 4666, 4705, 4759, 4828, 4898, 4945, 5000, 5076, 5140, 5190, 5263, 5313, 5368, 5422, 5527]}, {"subject_area"=>"/Biology and life sciences/Psychology", "average_usage"=>[300, 668, 856, 1029, 1149, 1267, 1378, 1465, 1567, 1675, 1742, 1802, 1846, 1918, 1996, 2068, 2152, 2228, 2308, 2373, 2431, 2522, 2590, 2666, 2736, 2790, 2837, 2887, 2941, 3002, 3066, 3116, 3181, 3233, 3277, 3301, 3330, 3369, 3411, 3456, 3501, 3534, 3628, 3702, 3741, 3783, 3848, 3903, 3958, 4010, 4067, 4133, 4174, 4209, 4245, 4295, 4375, 4422, 4554, 4596, 4633, 4675, 4732, 4801, 4851, 4912, 4958, 5042, 5091, 5138, 5190, 5262, 5346]}, {"subject_area"=>"/Computer and information sciences/Information technology", "average_usage"=>[276, 584, 734, 859, 1188, 1401, 1593, 1674, 1732, 1823, 1888, 1974, 2045, 2109, 2149, 2199, 2250, 2289, 2335, 2383, 2417, 2483, 2543, 2620, 2691, 2750, 2788, 2820, 2853, 2895, 2934, 2982, 3043, 3126, 3164, 3196, 3221, 3261, 3282, 3308, 3327, 3356, 3395, 3422, 3453, 3483, 3508, 3533, 3573, 3599, 3626, 3652, 3671, 3699, 3725, 3770, 3795, 3858, 3916, 3974, 4007, 4078, 4312, 4368, 4465, 4570, 4662, 4789, 4873, 4966, 5060, 5078, 5106, 5115, 5139]}, {"subject_area"=>"/Social sciences", "average_usage"=>[340, 737, 959, 1129, 1262, 1394, 1513, 1582, 1669, 1714, 1825, 1880, 1942, 2002, 2072, 2191, 2264, 2326, 2393, 2427, 2506, 2596, 2666, 2738, 2817, 2889, 2943, 2989, 3036, 3107, 3159, 3194, 3240, 3284, 3322, 3378, 3405, 3458, 3507, 3537, 3574, 3625, 3685, 3755, 3819, 3872, 3941, 3988, 4042, 4100, 4149, 4244, 4300, 4349, 4397, 4460, 4542, 4617, 4680, 4742, 4807, 4864, 4924, 4974, 5030, 5069, 5113, 5136, 5168, 5241, 5292, 5335, 5399]}, {"subject_area"=>"/Social sciences/Psychology", "average_usage"=>[300, 668, 856, 1029, 1149, 1267, 1378, 1465, 1567, 1675, 1742, 1802, 1846, 1918, 1996, 2068, 2152, 2228, 2308, 2373, 2431, 2522, 2590, 2666, 2736, 2790, 2837, 2887, 2941, 3002, 3066, 3116, 3181, 3233, 3277, 3301, 3330, 3369, 3411, 3456, 3501, 3534, 3628, 3702, 3741, 3783, 3848, 3903, 3958, 4010, 4067, 4133, 4174, 4209, 4245, 4295, 4375, 4422, 4554, 4596, 4633, 4675, 4732, 4801, 4851, 4912, 4958, 5042, 5091, 5138, 5190, 5262, 5346]}]}
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