E-LIB Suche - Ergebnisse für: Ramakrishnan, Kandan

1

Multi-Moments in Time: Learning and Interpreting Models for..:

Monfort, Mathew ; Pan, Bowen ; Ramakrishnan, Kandan...
IEEE Transactions on Pattern Analysis and Machine Intelligence. 44 (2022) 12 - p. 9434-9445 , 2022

Link: https://doi.org/10.1109/..

2

Deep Analysis of CNN-based Spatio-temporal Representations ..:

, In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),

Chen, Chun-Fu Richard ; Panda, Rameswar ; Ramakrishnan, Kandan... - p. 6161-6171 , 2021

Link: https://doi.org/10.1109/..

3

Moments in Time Dataset: One Million Videos for Event Under..:

Monfort, Mathew ; Vondrick, Carl ; Oliva, Aude...
IEEE Transactions on Pattern Analysis and Machine Intelligence. 42 (2020) 2 - p. 502-508 , 2020

Link: https://doi.org/10.1109/..

4

Relationship Matters: Relation Guided Knowledge Transfer fo..:

, In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),

Ramakrishnan, Kandan ; Panda, Rameswar ; Fan, Quanfu... - p. 1009-1018 , 2020

Link: https://doi.org/10.1109/..

5

Examining Class Dependant Sub-Paths in Deep Neural Networks:

Monfort, Mathew ; Ramakrishnan, Kandan ; Andonian, Alex.
Journal of Vision. 19 (2019) 10 - p. 28b , 2019

Link: https://doi.org/10.1167/..

6

A cognitively-aligned representational space for DNNs:

Ramakrishnan, Kandan ; Mohsenzadeh, Yalda ; Monfort, Mathew.
Journal of Vision. 19 (2019) 10 - p. 61 , 2019

Link: https://doi.org/10.1167/..

7

A Large Scale Video Dataset for Event Recognition:

Monfort, Mathew ; Zhou, Bolei ; Bargal, Sarah...
Journal of Vision. 18 (2018) 10 - p. 753 , 2018

Link: https://doi.org/10.1167/..

8

Visual pathways from the perspective of cost functions and ..:

Scholte, H. Steven ; Losch, Max M. ; Ramakrishnan, Kandan..
Cortex. 98 (2018) - p. 249-261 , 2018

Link: https://doi.org/10.1016/..

9

Overlap in performance of CNN's, human behavior and EEG cla..:

Seijdel, Noor ; Ramakrishnan, Kandan ; Losch, Max.
Journal of Vision. 16 (2016) 12 - p. 501 , 2016

Link: https://doi.org/10.1167/..

10

Feature representations in networks trained with image sets..:

Losch, Max ; Seijdel, Noor ; Ramakrishnan, Kandan..
Journal of Vision. 16 (2016) 12 - p. 175 , 2016

Link: https://doi.org/10.1167/..

11

What is unique in computational models of object recognitio..:

Ramakrishnan, Kandan ; Scholte, H.Steven ; Ghebreab, Sennay
Journal of Vision. 16 (2016) 12 - p. 757 , 2016

Link: https://doi.org/10.1167/..

12

Mapping human visual representations by deep neural network:

Ramakrishnan, Kandan ; Scholte, H. Steven ; Smeulders, Arnold.
Journal of Vision. 16 (2016) 12 - p. 373 , 2016

Link: https://doi.org/10.1167/..

13

CNNs trained on places and animacy explain different patter..:

Scholte, H.Steven ; Losch, Max ; Seijdel, Noor..
Journal of Vision. 16 (2016) 12 - p. 758 , 2016

Link: https://doi.org/10.1167/..

14

Visual dictionaries as intermediate features in the human b..:

Ramakrishnan, Kandan ; Scholte, H. Steven ; Groen, Iris I. A...
Frontiers in Computational Neuroscience. 8 (2015) - p. , 2015

Link: https://doi.org/10.3389/..

15

Localization of Handwritten Text in Documents Using Moment ..:

, In: International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007),

Ramakrishnan, Kandan ; Arvind, K.R. ; Ramakrishnan, A.G. - p. None , 2007

Link: https://doi.org/10.1109/..