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20162025
most citedLP-3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks

5 citations · 13 across the 20 of their papers we have counts for

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Showing 2019 · cs.CVShow all

7 papers · 2 filters

cs.CV2019

FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network

Zeeshan Khan, Mukul Khanna, Shanmuganathan Raman

High dynamic range (HDR) image generation from a single exposure low dynamic range (LDR) image has been made possible due to the recent advances in Deep Learning. Various feed-forw…

cs.CV2019

Simultaneous Detection and Removal of Dynamic Objects in Multi-view Images

Gagan Kanojia, Shanmuganathan Raman

Consider a set of images of a scene consisting of moving objects captured using a hand-held camera. In this work, we propose an algorithm which takes this set of multi-view images…

cs.CV2019

DeepPFCN: Deep Parallel Feature Consensus Network For Person Re-Identification

Shubham Kumar Singh, Krishna P Miyapuram, Shanmuganathan Raman

Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-ident…

cs.CV2019

Exploring Temporal Differences in 3D Convolutional Neural Networks

Gagan Kanojia, Sudhakar Kumawat, Shanmuganathan Raman

Traditional 3D convolutions are computationally expensive, memory intensive, and due to large number of parameters, they often tend to overfit. On the other hand, 2D CNNs are less…

cs.CV2019

Attentive Spatio-Temporal Representation Learning for Diving Classification

Gagan Kanojia, Sudhakar Kumawat, Shanmuganathan Raman

Competitive diving is a well recognized aquatic sport in which a person dives from a platform or a springboard into the water. Based on the acrobatics performed during the dive, di…

cs.CV2019

LBVCNN: Local Binary Volume Convolutional Neural Network for Facial Expression Recognition from Image Sequences

Sudhakar Kumawat, Manisha Verma, Shanmuganathan Raman

Recognizing facial expressions is one of the central problems in computer vision. Temporal image sequences have useful spatio-temporal features for recognizing expressions. In this…