Subhransu Maji

University of Massachusetts Amherst
0000-0002-3869-9334
http://people.cs.umass.edu/~smaji/
Publications125
h-index40
Citations11,968
Highly Influential Citations1,694
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  • Publications
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Multi-view Convolutional Neural Networks for 3D Shape Recognition
TLDR
This work presents a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and shows that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art3D shape descriptors. Expand
Bilinear CNN Models for Fine-Grained Visual Recognition
We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an… Expand
Describing Textures in the Wild
TLDR
This work identifies a vocabulary of forty-seven texture terms and uses them to describe a large dataset of patterns collected "in the wild", and shows that they both outperform specialized texture descriptors not only on this problem, but also in established material recognition datasets. Expand
Fine-Grained Visual Classification of Aircraft
TLDR
Compared to the domains usually considered in fine-grained visual classification (FGVC), for example animals, aircraft are rigid and hence less deformable, however, they present other interesting modes of variation, including purpose, size, designation, structure, historical style, and branding. Expand
Classification using intersection kernel support vector machines is efficient
TLDR
It is shown that one can build histogram intersection kernel SVMs (IKSVMs) with runtime complexity of the classifier logarithmic in the number of support vectors as opposed to linear for the standard approach. Expand
Semantic contours from inverse detectors
TLDR
A simple yet effective method for combining generic object detectors with bottom-up contours to identify object contours is presented and a principled way of combining information from different part detectors and across categories is provided. Expand
Meta-Learning With Differentiable Convex Optimization
TLDR
The objective is to learn feature embeddings that generalize well under a linear classification rule for novel categories and this work exploits two properties of linear classifiers: implicit differentiation of the optimality conditions of the convex problem and the dual formulation of the optimization problem. Expand
Deep filter banks for texture recognition and segmentation
TLDR
This work proposes a new texture descriptor, FV-CNN, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank, which substantially improves the state-of-the-art in texture, material and scene recognition. Expand
Max-margin additive classifiers for detection
TLDR
A pair of fast training algorithms for piece-wise linear classifiers, which can approximate arbitrary additive models, are presented, which are trained in a max-margin framework and significantly outperform linear classifier on a variety of vision datasets. Expand
Deep Filter Banks for Texture Recognition, Description, and Segmentation
TLDR
A human-interpretable vocabulary of texture attributes to describe common texture patterns is proposed, complemented by a new describable texture dataset for benchmarking and state-of-the-art performance in numerous datasets well beyond textures are obtained. Expand
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