computer-vision-7

Fine-Grained Visual Recognition

3260 papers • 126 benchmarks • 313 datasets

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gofynd/mildnet

2 papers 84

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Most implemented papers

Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

Yin Cui, Serge J. Belongie, Hartwig Adam, B. Hariharan, Claire Cardie, Menglin Jia, Mengyun Shi, Mikhail Sirotenko•Sat Apr 25 2020

This work proposes a novel Attribute-Mask RCNN model to jointly perform instance segmentation and localized attribute recognition, and provides a novel evaluation metric for the task.

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Bilinear CNNs for Fine-grained Visual Recognition

Subhransu Maji, Tsung-Yu Lin, Aruni RoyChowdhury•Tue Apr 28 2015

These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner and can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture.

94 0

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Retrieving Similar E-Commerce Images Using Deep Learning

Rishab Sharma, Anirudha Vishvakarma•Thu Jan 10 2019

A deep siamese architecture that when trained on positive and negative pairs of images learn an embedding that accurately approximates the ranking of images in order of visual similarity notion is presented.

21 0

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Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization

W. Zuo, Lei Zhang, Qilong Wang, P. Li, Jiangtao Xie•Sun Apr 14 2019

The proposed MPN-COV conforms to a robust covariance estimator, very suitable for scenario of high dimension and small sample size, and can be regarded as Power-Euclidean metric between covariances, effectively exploiting their geometry.

114 0

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Metric Learning with Adaptive Density Discrimination

Lubomir D. Bourdev, Manohar Paluri, Piotr Dollár, Oren Rippel•Tue Nov 17 2015

This work proposes a novel approach explicitly designed to address a number of subtle yet important issues which have stymied earlier DML algorithms, which maintains an explicit model of the distributions of the different classes in representation space and employs this knowledge to adaptively assess similarity, and achieve local discrimination by penalizing class distribution overlap.

219 0

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Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

Xinge You, Chaojian Yu, Xinyi Zhao, Qi Zheng, Peng Zhang•Wed Jul 25 2018

A cross-layer bilinear pooling approach is proposed to capture the inter-layer part feature relations, which results in superior performance compared with other bilinears pooling based approaches.

319 0

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MILDNet: A Lightweight Single Scaled Deep Ranking Architecture

Anirudha Vishvakarma•Sat Mar 02 2019

This paper proposes a competing novel CNN architecture, called MILDNet, which merits by being vastly compact (about 3 times), and Inspired by the fact that successive CNN layers represent the image with increasing levels of abstraction, compressed the authors' deep ranking model to a single CNN by coupling activations from multiple intermediate layers along with the last layer.

3 0

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X-Linear Attention Networks for Image Captioning

Ting Yao, Yingwei Pan, Yehao Li, Tao Mei•Mon Mar 30 2020

A unified attention block --- X-Linear attention block, that fully employs bilinear pooling to selectively capitalize on visual information or perform multi-modal reasoning is introduced.

609 0

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Feathers dataset for fine-grained visual categorization

A. Belko, K. Dobratulin, A. Kuznetsov•Fri Apr 17 2020

A novel dataset FeatherV1, containing 28,272 images of feathers categorized by 595 bird species, was created to perform taxonomic identification of bird species by a single feather, which can be applied in amateur and professional ornithology.

3 0

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CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

Marcos V. Conde, Kerem Turgutlu•Mon May 31 2021

This work uses CLIP (Contrastive Language-Image Pre-Training) for training a neural network on a variety of art images and text pairs, being able to learn directly from raw descriptions about images, or if available, curated labels, with zero-shot capability.

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