66 citations · 124 across the 6 of their papers we have counts for
6 papers
Visualizing and Describing Fine-grained Categories as Textures
Tsung-Yu Lin, Mikayla Timm, Chenyun Wu +1
We analyze how categories from recent FGVC challenges can be described by their textural content. The motivation is that subtle differences between species of birds or butterflies…
Second-order Democratic Aggregation
Tsung-Yu Lin, Subhransu Maji, Piotr Koniusz
Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classificatio…
Improved Bilinear Pooling with CNNs
Tsung-Yu Lin, Subhransu Maji
Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23], and their compact variants [10], have been shown to be effective at fine-grained recognition, scene catego…
Visualizing and Understanding Deep Texture Representations
Tsung-Yu Lin, Subhransu Maji
A number of recent approaches have used deep convolutional neural networks (CNNs) to build texture representations. Nevertheless, it is still unclear how these models represent tex…
One-to-many face recognition with bilinear CNNs
Aruni RoyChowdhury, Tsung-Yu Lin, Subhransu Maji +1
The recent explosive growth in convolutional neural network (CNN) research has produced a variety of new architectures for deep learning. One intriguing new architecture is the bil…
Bilinear CNNs for Fine-grained Visual Recognition
Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji
We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a po…