148 citations · 310 across the 24 of their papers we have counts for
7 papers · 1 filter
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Anirban Santara, Kaustubh Mani, Pranoot Hatwar +4
Deep learning based landcover classification algorithms have recently been proposed in literature. In hyperspectral images (HSI) they face the challenges of large dimensionality, s…
Recurrent Memory Addressing for describing videos
Arnav Kumar Jain, Abhinav Agarwalla, Kumar Krishna Agrawal +1
In this paper, we introduce Key-Value Memory Networks to a multimodal setting and a novel key-addressing mechanism to deal with sequence-to-sequence models. The proposed model natu…
WEPSAM: Weakly Pre-Learnt Saliency Model
Avisek Lahiri, Sourya Roy, Anirban Santara +2
Visual saliency detection tries to mimic human vision psychology which concentrates on sparse, important areas in natural image. Saliency prediction research has been traditionally…
Visual saliency detection: a Kalman filter based approach
Sourya Roy, Pabitra Mitra
In this paper we propose a Kalman filter aided saliency detection model which is based on the conjecture that salient regions are considerably different from our "visual expectatio…
Visualization Regularizers for Neural Network based Image Recognition
Biswajit Paria, Vikas Reddy, Anirban Santara +1
The success of deep neural networks is mostly due their ability to learn meaningful features from the data. Features learned in the hidden layers of deep neural networks trained in…
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
Debapriya Maji, Anirban Santara, Pabitra Mitra +1
Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale sc…
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training
Anirban Santara, Debapriya Maji, DP Tejas +2
Deep neural networks are capable of modelling highly non-linear functions by capturing different levels of abstraction of data hierarchically. While training deep networks, first t…