| Year |
Citation |
Score |
| 2024 |
Ahuja A, Yusif Rodriguez N, Ashok AK, Serre T, Desrochers TM, Sheinberg DL. Monkeys engage in visual simulation to solve complex problems. Current Biology : Cb. PMID 39549702 DOI: 10.1016/j.cub.2024.10.026 |
0.68 |
|
| 2024 |
Ahuja A, Rodriguez NY, Ashok AK, Serre T, Desrochers T, Sheinberg D. Monkeys engage in visual simulation to solve complex problems. Biorxiv : the Preprint Server For Biology. PMID 38464308 DOI: 10.1101/2024.02.21.581495 |
0.68 |
|
| 2023 |
Fel T, Picard A, Bethune L, Boissin T, Vigouroux D, Colin J, Cadène R, Serre T. CRAFT: Concept Recursive Activation FacTorization for Explainability. Proceedings. Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2023: 2711-2721. PMID 38463608 DOI: 10.1109/cvpr52729.2023.00266 |
0.766 |
|
| 2023 |
Colin J, Fel T, Cadène R, Serre T. What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods. Advances in Neural Information Processing Systems. 35: 2832-2845. PMID 37786623 |
0.754 |
|
| 2023 |
Zerroug A, Vaishnav M, Colin J, Musslick S, Serre T. A Benchmark for Compositional Visual Reasoning. Advances in Neural Information Processing Systems. 35: 29776-29788. PMID 37534101 |
0.368 |
|
| 2023 |
Fel T, Felipe I, Linsley D, Serre T. Harmonizing the object recognition strategies of deep neural networks with humans. Advances in Neural Information Processing Systems. 35: 9432-9446. PMID 37465369 |
0.821 |
|
| 2022 |
Vaishnav M, Cadene R, Alamia A, Linsley D, VanRullen R, Serre T. Understanding the Computational Demands Underlying Visual Reasoning. Neural Computation. 34: 1075-1099. PMID 35231926 DOI: 10.1162/neco_a_01485 |
0.431 |
|
| 2020 |
Alamia A, Luo C, Ricci M, Kim J, Serre T, VanRullen R. Differential involvement of EEG oscillatory components in sameness vs. spatial-relation visual reasoning tasks. Eneuro. PMID 33239271 DOI: 10.1523/ENEURO.0267-20.2020 |
0.803 |
|
| 2020 |
Kreiman G, Serre T. Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences. PMID 32112444 DOI: 10.1111/nyas.14320 |
0.693 |
|
| 2018 |
Mély DA, Linsley D, Serre T. Complementary surrounds explain diverse contextual phenomena across visual modalities. Psychological Review. PMID 30234321 DOI: 10.1037/rev0000109 |
0.803 |
|
| 2018 |
Kim J, Ricci M, Serre T. Not-So-CLEVR: learning same-different relations strains feedforward neural networks. Interface Focus. 8: 20180011. PMID 29951191 DOI: 10.1098/rsfs.2018.0011 |
0.806 |
|
| 2018 |
Linsley D, Shiebler D, Eberhardt S, Karagounis A, Serre T. Large-scale identification of the visual features used for object recognition with ClickMe.ai Journal of Vision. 18: 414. DOI: 10.1167/18.10.414 |
0.471 |
|
| 2017 |
Linsley D, Eberhardt S, Gupta P, Serre T. A novel game for discovering visual features for object recognition. Journal of Vision. 17: 1249. DOI: 10.1167/17.10.1249 |
0.6 |
|
| 2016 |
Serre T. Models of visual categorization. Wiley Interdisciplinary Reviews. Cognitive Science. PMID 26997155 DOI: 10.1002/wcs.1385 |
0.507 |
|
| 2016 |
Wilf P, Zhang S, Chikkerur S, Little SA, Wing SL, Serre T. Computer vision cracks the leaf code. Proceedings of the National Academy of Sciences of the United States of America. PMID 26951664 DOI: 10.1073/Pnas.1524473113 |
0.37 |
|
| 2015 |
Mély DA, Kim J, McGill M, Guo Y, Serre T. A systematic comparison between visual cues for boundary detection. Vision Research. PMID 26748113 DOI: 10.1016/j.visres.2015.11.007 |
0.771 |
|
| 2015 |
Parker SM, Serre T. Unsupervised invariance learning of transformation sequences in a model of object recognition yields selectivity for non-accidental properties. Frontiers in Computational Neuroscience. 9: 115. PMID 26500528 DOI: 10.3389/fncom.2015.00115 |
0.386 |
|
| 2015 |
Cauchoix M, Crouzet SM, Fize D, Serre T. Fast ventral stream neural activity enables rapid visual categorization. Neuroimage. 125: 280-290. PMID 26477655 DOI: 10.1016/J.Neuroimage.2015.10.012 |
0.509 |
|
| 2015 |
Sofer I, Crouzet SM, Serre T. Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization. Plos Computational Biology. 11: e1004456. PMID 26335683 DOI: 10.1371/journal.pcbi.1004456 |
0.41 |
|
| 2015 |
Mély D, Serre T. A canonical circuit for visual contextual integration explains induction effects across visual modalities. Journal of Vision. 15: 1121. PMID 26326809 DOI: 10.1167/15.12.1121 |
0.444 |
|
| 2014 |
Cauchoix M, Barragan-Jason G, Serre T, Barbeau EJ. The neural dynamics of face detection in the wild revealed by MVPA. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 34: 846-54. PMID 24431443 DOI: 10.1523/Jneurosci.3030-13.2014 |
0.38 |
|
| 2014 |
Parker S, Reichert D, Serre T. Selectivity for non-accidental properties emerges from learning object transformation sequences Journal of Vision. 14: 910-910. DOI: 10.1167/14.10.910 |
0.541 |
|
| 2014 |
Mely DA, Kim J, McGill M, Guo Y, Serre T. Visual cue diagnosticity for boundary detection in natural scenes: A computational study Journal of Vision. 14: 889-889. DOI: 10.1167/14.10.889 |
0.433 |
|
| 2014 |
Barhomi Y, Yanke A, Bonneaud S, Warren W, Serre T. A data-driven approach to learning strategies for the visual control of navigation. Journal of Vision. 14: 1347-1347. DOI: 10.1167/14.10.1347 |
0.387 |
|
| 2013 |
Poggio T, Serre T. Models of visual cortex Scholarpedia. 8: 3516. DOI: 10.4249/scholarpedia.3516 |
0.719 |
|
| 2013 |
Sofer I, Lee KR, Sailamul P, Crouzet S, Serre T. Understanding the nature of the visual representations underlying rapid categorization tasks. Journal of Vision. 13: 658-658. DOI: 10.1167/13.9.658 |
0.463 |
|
| 2013 |
Heindel W, Festa E, Ott B, Sofer I, Serre T. P2-255: Rapid visual categorization as a sensitive measure of early Alzheimer's disease Alzheimer's & Dementia. 9: P451-P452. DOI: 10.1016/J.Jalz.2013.05.902 |
0.338 |
|
| 2013 |
Tan C, Singer JM, Serre T, Sheinberg D, Poggio TA. Neural representation of action sequences: How far can a simple snippet-matching model take us? Advances in Neural Information Processing Systems. |
0.745 |
|
| 2012 |
Sofer I, Serre T. Using decision models to study the time course of visual recognition Journal of Vision. 12: 160-160. DOI: 10.1167/12.9.160 |
0.422 |
|
| 2012 |
Jhuang H, Garrote E, Yu X, Khilnani V, Poggio T, Steele AD, Serre T. Erratum: Corrigendum: Automated home-cage behavioural phenotyping of mice Nature Communications. 3. DOI: 10.1038/Ncomms1399 |
0.588 |
|
| 2012 |
Cauchoix M, Arslan AB, Fize D, Serre T. The neural dynamics of visual processing in monkey extrastriate cortex: A comparison between univariate and multivariate techniques Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7263: 164-171. DOI: 10.1007/978-3-642-34713-9_21 |
0.706 |
|
| 2011 |
Crouzet SM, Serre T. What are the Visual Features Underlying Rapid Object Recognition? Frontiers in Psychology. 2: 326. PMID 22110461 DOI: 10.3389/fpsyg.2011.00326 |
0.564 |
|
| 2011 |
Zhang Y, Meyers EM, Bichot NP, Serre T, Poggio TA, Desimone R. Object decoding with attention in inferior temporal cortex. Proceedings of the National Academy of Sciences of the United States of America. 108: 8850-5. PMID 21555594 DOI: 10.1073/pnas.1100999108 |
0.814 |
|
| 2011 |
Arslan AB, Singer J, Cauchoix M, Madsen J, Kreiman G, Serre T. The neural basis of rapid visual recognition: Neural decoding in time and spectral domains F1000research. 2. DOI: 10.7490/F1000Research.1407.1 |
0.787 |
|
| 2011 |
Tan C, Serre T, Poggio T. How does the visual system create complex shape and motion features? Journal of Vision. 11: 888-888. DOI: 10.1167/11.11.888 |
0.809 |
|
| 2011 |
Sofer I, Weinshall D, Serre T. Analysis of similarity matrices and its application to the study of semantic and visual information processing in the inferior temporal cortex Journal of Vision. 11: 872-872. DOI: 10.1167/11.11.872 |
0.677 |
|
| 2011 |
Arslan A, Singer J, Cauchoix M, Madsen J, Kreiman G, Serre T. The neural basis of rapid visual recognition: Neural decoding and Granger causality analysis of connectivity Journal of Vision. 11: 824-824. DOI: 10.1167/11.11.824 |
0.802 |
|
| 2011 |
Corbett J, Serre T. ERP signatures of Gestalt cues predict perceptual segmentation Journal of Vision. 11: 1078-1078. DOI: 10.1167/11.11.1078 |
0.32 |
|
| 2011 |
Jhuang H, Garrote E, Edelman N, Poggio T, Steele A, Serre T. Trainable, vision-based automated home cage behavioral phenotyping Acm International Conference Proceeding Series. DOI: 10.1145/1931344.1931377 |
0.579 |
|
| 2011 |
Chikkerur S, Serre T, Tan C, Poggio T. Attention as a Bayesian inference process Proceedings of Spie - the International Society For Optical Engineering. 7865. DOI: 10.1117/12.876734 |
0.817 |
|
| 2011 |
Kuehne H, Jhuang H, Garrote E, Poggio T, Serre T. HMDB: A large video database for human motion recognition Proceedings of the Ieee International Conference On Computer Vision. 2556-2563. DOI: 10.1109/ICCV.2011.6126543 |
0.612 |
|
| 2010 |
Jhuang H, Garrote E, Mutch J, Yu X, Khilnani V, Poggio T, Steele AD, Serre T. Automated home-cage behavioural phenotyping of mice. Nature Communications. 1: 68. PMID 20842193 DOI: 10.1038/Ncomms1064 |
0.598 |
|
| 2010 |
Chikkerur S, Serre T, Tan C, Poggio T. What and where: a Bayesian inference theory of attention. Vision Research. 50: 2233-47. PMID 20493206 DOI: 10.1016/J.Visres.2010.05.013 |
0.82 |
|
| 2010 |
Reddy L, Tsuchiya N, Serre T. Reading the mind's eye: decoding category information during mental imagery. Neuroimage. 50: 818-25. PMID 20004247 DOI: 10.1016/J.Neuroimage.2009.11.084 |
0.46 |
|
| 2010 |
Serre T, Reddy L, Tsuchyia N, Poggio T, Fabre-Thorpe M, Koch C. Reading the mind's eye: Decoding object information during mental imagery from fMRI patterns Journal of Vision. 9: 782-782. DOI: 10.1167/9.8.782 |
0.683 |
|
| 2010 |
Wyble B, Potter M, Serre T, Giese M. Identification of point light walkers exhibits an attentional blink Journal of Vision. 9: 620-620. DOI: 10.1167/9.8.620 |
0.503 |
|
| 2010 |
Tan C, Serre T, Chikkerur S, Poggio T. Feature-based and contextual guidance mechanisms in complex natural visual search Journal of Vision. 9: 1189-1189. DOI: 10.1167/9.8.1189 |
0.817 |
|
| 2010 |
Tan C, Serre T, Kreiman G, Poggio T. Implicit coding of location, scale and configural information in feedforward hierarchical models of the visual cortex Journal of Vision. 8: 43-43. DOI: 10.1167/8.6.43 |
0.799 |
|
| 2010 |
Masquelier T, Serre T, Thorpe S, Poggio T. Learning simple and complex cells-like receptive fields from natural images: a plausibility proof Journal of Vision. 7: 81-81. DOI: 10.1167/7.9.81 |
0.699 |
|
| 2010 |
Kreiman G, Serre T, Poggio T. On the limits of feed-forward processing in visual object recognition Journal of Vision. 7: 1041-1041. DOI: 10.1167/7.9.1041 |
0.813 |
|
| 2010 |
Serre T, Oliva A, Poggio T. Feedforward theories of visual cortex predict human performance in rapid categorization Journal of Vision. 6: 615-615. DOI: 10.1167/6.6.615 |
0.711 |
|
| 2010 |
Sigala R, Serre T, Poggio T, Giese MA. Learning mid-level motion features for the recognition of body movements Journal of Vision. 5: 26-26. DOI: 10.1167/5.8.26 |
0.734 |
|
| 2010 |
Cauchoix M, Serre T, Kreiman G, Fize D. Fast decoding of natural object categories from intracranial field potentials in monkey's visual cortex Journal of Vision. 10: 947-947. DOI: 10.1167/10.7.947 |
0.679 |
|
| 2010 |
Tan C, Singer J, Serre T, Sheinberg D, Poggio T. How STS recognizes actions: Predicting single-neuron responses in higher visual cortex Journal of Vision. 10: 935-935. DOI: 10.1167/10.7.935 |
0.79 |
|
| 2010 |
Serre T, Poggio T. A neuromorphic approach to computer vision Communications of the Acm. 53: 54-61. DOI: 10.1145/1831407.1831425 |
0.658 |
|
| 2010 |
Kliper R, Serre T, Weinshall D, Nelken I. The story of a single cell: Peeking into the semantics of spikes 2010 2nd International Workshop On Cognitive Information Processing, Cip2010. 281-286. DOI: 10.1109/CIP.2010.5604119 |
0.509 |
|
| 2007 |
Serre T, Kreiman G, Kouh M, Cadieu C, Knoblich U, Poggio T. A quantitative theory of immediate visual recognition. Progress in Brain Research. 165: 33-56. PMID 17925239 DOI: 10.1016/S0079-6123(06)65004-8 |
0.814 |
|
| 2007 |
Serre T, Oliva A, Poggio T. A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences of the United States of America. 104: 6424-9. PMID 17404214 DOI: 10.1073/pnas.0700622104 |
0.754 |
|
| 2007 |
Serre T, Wolf L, Bileschi S, Riesenhuber M, Poggio T. Robust object recognition with cortex-like mechanisms. Ieee Transactions On Pattern Analysis and Machine Intelligence. 29: 411-26. PMID 17224612 DOI: 10.1109/Tpami.2007.56 |
0.79 |
|
| 2007 |
Serre T, Giese M. Rapid Serial Action Presentation: New paradigm for the study of movement recognition Journal of Vision. 7: 549-549. DOI: 10.1167/7.9.549 |
0.525 |
|
| 2007 |
Jhuang H, Serre T, Wolf L, Poggio T. A biologically inspired system for action recognition Proceedings of the Ieee International Conference On Computer Vision. DOI: 10.1109/ICCV.2007.4408988 |
0.607 |
|
| 2007 |
Heisele B, Serre T, Poggio T. A component-based framework for face detection and identification International Journal of Computer Vision. 74: 167-181. DOI: 10.1007/s11263-006-0006-z |
0.65 |
|
| 2005 |
Serre T, Poggio T. Standard model v2.0: How visual cortex might learn a universal dictionary of shape components Journal of Vision. 5: 742-742. DOI: 10.1167/5.8.742 |
0.706 |
|
| 2005 |
Walther D, Serre T, Poggio T, Koch C. Modeling feature sharing between object detection and top-down attention Journal of Vision. 5: 1041-1041. DOI: 10.1167/5.8.1041 |
0.769 |
|
| 2005 |
Serre T, Wolf L, Poggio T. Object recognition with features inspired by visual cortex Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2: 994-1000. DOI: 10.1109/CVPR.2005.254 |
0.726 |
|
| 2005 |
Sigala R, Serre T, Poggio T, Giese M. Learning features of intermediate complexity for the recognition of biological motion Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3696: 241-246. DOI: 10.1007/11550822_39 |
0.703 |
|
| 2003 |
Heisele B, Serre T, Prentice S, Poggio T. Hierarchical classification and feature reduction for fast face detection with support vector machines Pattern Recognition. 36: 2007-2017. DOI: 10.1016/S0031-3203(03)00062-1 |
0.673 |
|
| 2002 |
Heisele B, Serre T, Pontil M, Vetter T, Poggio T. Categorization by learning and combining object parts Advances in Neural Information Processing Systems. |
0.628 |
|
| 2002 |
Serre T, Riesenhuber M, Louie J, Poggio T. On the role of object-specific features for real world object recognition in biological vision Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2525: 387-397. |
0.776 |
|
| 2001 |
Heisele B, Serre T, Mukherjee S, Poggio T. Feature reduction and hierarchy of classifiers for fast object detection in video images Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2: II18-II24. |
0.645 |
|
| 2001 |
Heisele B, Serre T, Pontil M, Poggio T. Component-based face detection Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 1: I657-I662. |
0.594 |
|
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