Hi there ! I am curious about how the brain solves vision. I use a variety of approaches to study visual processing and these include visual psychophysics, computational models and neural recordings. I am excited about the mutual benefits of understanding human vision and machine vision! I am currently part of the Laboratory for Neuropsychology (LN) in NIMH/NIH.
Representative publications with relevant summary
Object and scene vision Deep learning / Computer vision Faces Modeling cognitive states Developmental disorders
Training, education and skillset
Research skillset Training in academia and industry Grants & Financial awards
Research themes
Experimental vision
G Jacob, RT Pramod, H Katti, SP Arun. , Qualitative similarities and differences in visual object representations between brains and deep networks, Nature Communications, 2021, https://www.nature.com/articles/s41467-021-22078-3 . Featured in the AI and machine learning focus https://www.nature.com/collections/ceiajcdbeb
Harish Katti; S. P. Arun, Are you from North or South India? A hard face-classification task reveals systematic representational differences between humans and machines Journal of Vision. 2019;19(7):1 doi:10.1167/19.7.1 (research article) Code and dataset: https://github.com/harish2006/IISCIFD
H Katti, MV Peelen, SP Arun, Machine vision benefits from human contextual expectations, Scientific reports 9 (1), 2112 (research article) Code and dataset: https://github.com/harish2006/cntxt_likelihood
Harish Katti; S. P. Arun, Monkeys can’t read but their brains can: Compositionality and CAPTCHA decoding in IT neurons. Vision: Representation of Objects and Scenes, Annual meeting of the Society for Neuroscience, San Diego, USA, 2018 (Oral presentation at SfN 2018)
G Jacob, RT Pramod, H Katti, SP Arun. Comparing perception in deep neural networks and humans, Journal of Vision 18 (10), 900-900 (Oral presentation at VSS 2018)
H Katti, MV Peelen, SP Arun, How do targets, nontargets, and scene context influence real-world object detection? Attention, Perception, & Psychophysics 79 (7), 2021-2036 (research article)
H Katti, M Peelen, SP Arun, Expecting and detecting objects in real-world scenes: when do target, nontarget and coarse scene features contribute?, Journal of Vision 17 (10), 299-299 (Poster at VSS 2017)
H Katti, NC Puneeth, SP Arun, Competitive interactions between rule and association learning during face categorization, SfN, 2014, San DIego, USA (Poster at SfN 2014)
H Katti, An information theory based technique to improve eye fixation clustering and salient region discover, Journal of Vision 13 (9), 797-797 (Poster at VSS 2011)
C Lang*, TV Nguyen*, H Katti*, K Yadati, M Kankanhalli, S Yan, Depth matters: Influence of depth cues on visual saliency, European conference on computer vision, 101-115 (research article) NUS3D-Saliency Dataset https://sites.google.com/site/vantam/nus3d-saliency-dataset
S Ramanathan, H Katti, N Sebe, M Kankanhalli, TS Chua, An eye fixation database for saliency detection in images, Computer Vision–ECCV 2010, 30-43 (research article) NUSEF eye fixation dataset, http://ncript.comp.nus.edu.sg/site/mmas/NUSEF.html
H Katti, KY Bin, TS Chua, M Kankanhalli, Pre-attentive discrimination of interestingness in images, 2008 IEEE International Conference on Multimedia and Expo, 1433-1436 (research article)
Computer vision/Multimedia/Computational Advertising
A Shukla, H Katti, M Kankanhalli, R Subramanian, Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements, ICMI, 2018 (research article)
A Shukla, SS Gullapuram, H Katti, K Yadati, M Kankanhalli,, Evaluating content-centric vs. user-centric ad affect recognition, Proceedings of the 19th ACM ICMI, 5 2017 (research article)
A Shukla, SS Gullapuram, H Katti, K Yadati, M Kankanhalli, Affect recognition in ads with application to computational advertising, Proceedings of the 25th ACM MM, 1148-1156 (research article)
H Katti, AK Rajagopal, M Kankanhalli, R Kalpathi, Online estimation of evolving human visual interest, ACM TOMCCAP, 12, 2014 (research article)
K Yadati, H Katti, M Kankanhalli, Interactive video advertising: A multimodal affective approach,International Conference on Multimedia Modeling, 106-117 (research article)
K Yadati, H Katti, M Kankanhalli, CAVVA: Computational affective video-in-video advertising, IEEE Transactions on Multimedia 16 (1), 15-23 (research article)
HCI/Interactive systems
H Katti, M Kankanhalli, Eye-tracking methodology and applications to images and video, Proceedings of the 19th ACM international conference on Multimedia, 641-642 (research article)
H Katti, K Yadati, M Kankanhalli, C Tat-Seng, Affective video summarization and story board generation using pupillary dilation and eye gaze,2011 IEEE International Symposium on Multimedia, 319-326 (research article)
H Katti, R Subramanian, M Kankanhalli, N Sebe, TS Chua, KR Ramakrishnan, Making computers look the way we look: exploiting visual attention for image understanding, Proceedings of the 18th ACM international conference on Multimedia, 667-670 (research article)
S Ramanathan, H Katti, R Huang, TS Chua, M Kankanhalli, Automated localization of affective objects and actions in images via caption text-cum-eye gaze analysis, ACM MM, 2010, 729-732 (research article)
Interventional therapies for Autism
Georgitta J. Valiyamattam, Harish Katti, Vinay K. Chaganti, Marguerite E. O’Haire & Virender Sachdeva, Do Animals Engage Greater Social Attention in Autism? An Eye Tracking Analysis, Frontiers in Psychology 11 (2020)
Georgitta J. Valiyamattam, Harish Katti, Vinay Chaganti, Marguerite E. O’Haire, Virender Sachdeva , Circumscribed Interests in Autism: Can Animals Potentially Re-engage Social Attention?, Annual APA meeting, 2019 (Poster)
Georgitta J. Valiyamattam , Harish Katti , Vinay Chaganti, Marguerite E. O’Haire, Virender Sachdeva, Do Animals engage greater Social Attention in Autism? An eye tracking analysis, ISAZ meeting 2019 (Oral presentation)
Pre-prints
A Shukla, SS Gullapuram, H Katti, M Kankanhalli, S Winkler, S Ramanathan, Recognition of Advertisement Emotions with Application to Computational Advertising,arXiv preprint arXiv:1904.01778 (research article, under review)
RT Pramod*, H Katti*, SP Arun, Human peripheral blur is optimal for object recognition, arXiv preprint arXiv:1807.08476 (research article, under review)
Datasets
Indian Institute of Science Indian face dataset, https://github.com/harish2006/IISCIFD
NUS3D-Saliency Dataset https://sites.google.com/site/vantam/nus3d-saliency-dataset
NUSEF eye fixation dataset, http://ncript.comp.nus.edu.sg/site/mmas/NUSEF.html
Talks / Oral presentations
SfN 2018, Annual meeting of the society for Neuroscience, San Diego, November 2018, https://www.sfn.org/Meetings/Neuroscience-2018
ACCS 2018, October, Guwahati, http://www.iitg.ac.in/clst/accs2018/
What human vision can learn from machine vision and vice-versa ?, IGCLA 2018, Manipal, http://igclamanipal.com/
What human vision can learn from machine vision and vice-versa ?, IIIT Hyderabad summer school on computer vision, Hyderabad, July 2018, http://cvit.iiit.ac.in/cvsummerschool2018/
Online Estimation of Evolving Human Visual Interest, Department of Information systems, University of Trento, 2014
Tutorial on eye-tracking methodology, ACM MM 2011
Use of Non-Conventional means for Media Content and analysis, Panel discussion, ACM Multimedia 2011, Scottsdale, Arizona
December, 2010, Yahoo Research machine learning summer school
Visual attention and Media semantics,November, 2010, CVAI lab, Department of Electrical engineering, IISc
Media semantics, CANALAVIST 2009, http://www.canalavist.org/ict-forum/abstractimages.html
Media semantics, Max Planck institute for Cybernetics, Tuebingen, 2008