85 citations · 248 across the 20 of their papers we have counts for
6 papers · 1 filter
Class Correlation affects Single Object Localization using Pre-trained ConvNets
Pokkalla Harsha Vardhan, Kunal Sekhri, Dipan K. Pal +1
The problem of object localization has become one of the mainstream problems of vision. Most of the algorithms proposed involve the design for the model to be specifically for loca…
Max-Margin Invariant Features from Transformed Unlabeled Data
Dipan K. Pal, Ashwin A. Kannan, Gautam Arakalgud +1
The study of representations invariant to common transformations of the data is important to learning. Most techniques have focused on local approximate invariance implemented with…
Faster Than Real-time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses
Chandrasekhar Bhagavatula, Chenchen Zhu, Khoa Luu +1
Facial alignment involves finding a set of landmark points on an image with a known semantic meaning. However, this semantic meaning of landmark points is often lost in 2D approach…
Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition
Chi Nhan Duong, Kha Gia Quach, Khoa Luu +2
Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages. In order to efficien…
How ConvNets model Non-linear Transformations
Dipan K. Pal, Marios Savvides
In this paper, we theoretically address three fundamental problems involving deep convolutional networks regarding invariance, depth and hierarchy. We introduce the paradigm of Tra…
Emergence of Selective Invariance in Hierarchical Feed Forward Networks
Dipan K. Pal, Vishnu Boddeti, Marios Savvides
Many theories have emerged which investigate how in- variance is generated in hierarchical networks through sim- ple schemes such as max and mean pooling. The restriction to max/me…